Systems and methods for whole-brain circuit-based neurostimulation totreat brain disorders

ABSTRACT

Targeted and individualized methods are provided herein for determining specific treatment sites based on an individual patient. Embodiments described herein may use functional and/or structural connections to create brain mapping of the patient and/or control groups having the same metrics as the patient, and combinations thereof in determining target locations for stimulation treatment.

CROSS-REFERENCE AND CLAIM OF PRIORITY

This application is a Continuation of International Patent ApplicationNo. PCT/US2023/68688 filed Jun. 19, 2023, which claims the benefit ofU.S. Provisional Patent Application No. 63/354,451 filed Jun. 22, 2022,and claims the benefit of U.S. Provisional Patent Application No.63/383,241 filed Nov. 10, 2022. The entirety of these applications areincorporated herein by reference.

BACKGROUND

Transcranial magnetic stimulation (TMS) is a non-invasive,non-pharmacological procedure for treating neuropsychiatric disorders,in which a magnetic coil is used to stimulate specific regions of thebrain. TMS uses short, magnetic field pulses to induce electricalcurrents in underlying cortical tissue. Through electromagneticinduction, the TMS coil induce an electric current inside the brain atpre-determined targets when a magnetic pulse is delivered to the coilthat is placed on top of the patient's skull.

In psychiatry, TMS has been successfully used in treating depression,anxiety disorders, including panic disorder and obsessive-compulsivedisorder (OCD). TMS has also been studied with a large number ofneuropsychiatric disorders including autism, eating disorder, substanceabuse and addiction, posttraumatic stress disorder (PTSD), Alzheimer'sdisease, coma recovery, stroke, tinnitus, multiple sclerosis, andneurorehabilitation. For treatment-resistant major depressive disorder,TMS has been most widely used in a high-frequency (HF) mode on the leftfrontal lobe region of the brain called the dorsolateral prefrontalcortex (DLPFC) effectively. It has also been used in low-frequency (LF)mode on the right DLPFC with complementary effectiveness.

TMS over the left dorsolateral prefrontal cortex (L-DLPFC) is an FDAapproved treatment for treatment-refractory depression (TRD). Thistreatment is only partially effective, with response and remission ratesof 41.2% and 35.3%, respectively. The FDA approved protocol for TRDidentifies the left DLPFC stimulation site by moving the coil 5 cmanterior to the “hand motor hotspot” (motor cortex) along the curvatureof the scalp. This approach provides only approximate targeting of theleft DLPFC, with no consistent differentiation among DLPFC subregions.After ten years of the FDA-approval of TMS therapy, there has been nosignificant increase in clinical success.

SUMMARY

Embodiments described herein are exemplary only. Features of theembodiments provide examples of different combination of features.However, the disclosure covers variations of the features in differentcombinations as well.

The embodiments herein include functional connectivity analysis betweenregions, between regions and networks, and between networks in order topersonalize TMS treatment. Embodiments comprise determining more thanone treatment location for administering TMS treatment.

Embodiments of determining a treatment location for treatment compriseidentifying one or more treatment regions and networks by comparingpatient information to a healthy control group to determine thoseregions that are outside a desired range as compared to a healthycontrol group. Embodiments of determining a treatment location fortreatment comprises identifying one or more treatment regions that havethe highest change in a given region for pre- to post-treatment forindividuals having the same characteristics as the patient. Thecharacteristics may be any combination of gender, age, age range,ethnicity, weight, symptoms, diagnosis, prior treatment, response toprior treatment, genetic factors, prior medical conditions, symptomsand/or diagnosis of the patient.

Embodiments of determining a treatment location comprise comparingattributes of fMRI data including amplitude and frequency variations ofthe spontaneous blood oxygenation level as it fluctuates over time. Thecomparisons of brain regions and networks are made based on covariationof the variations of the spontaneous blood oxygenation level over time.The covariation may include changes in frequency and amplitude of theblood oxygenation levels over time. The determination of a treatmentlocation for treatment comprises comparing, such as by taking theabsolute value of a difference of, the covariation of each brain regionto each other region, each brain region to each other network, and ofeach network to each other network.

The determination of a treatment location for treatment comprises usingamplitude and frequency of brain activity within and between networks,between regions, and/or between regions and networks. Embodiments hereinmay use resting-stated functional connectivity to assess brain activity.Systems and methods herein may comprise Magnetic Resonance Imaging (MRI)systems. The resting-state functional connectivity may be based onchanges in the blood oxygenation level of the brain over time.Embodiments herein may use blood oxygenation level dependent (BOLD)imaging to generate images in functional magnetic resonance imaging(fMRI).

TMS treatment systems and methods are provided herein that determinenetworks and regions within a patient's brain for administering TMStreatments. Although explained herein in terms of TMS treatment,exemplary embodiments are not so limited. In exemplary embodiments, thetreatment may also or alternatively include low intensity focusedultrasound (LIFUS). In an exemplary embodiment, if the resulting brainregion and/or network for treatment is identified as a cortical braincircuit, then the location may be stimulated with TMS. If the brainregion and/or network for treatment is identified as a subcortical braincircuit, then the location may be stimulated with LIFUS. Other protocolsand treatments may also be used, such as, for example electricalstimulation.

Systems and methods may be configured to analyze the frequency,amplitude, and frequency relative to amplitude (or vice versa) changesbased on the fluctuations of the spontaneous blood oxygenationlevel-dependent as determined from an MRI of a patient's brain. Thecomparisons are based on the regions and networks of the brain. Thesystem may be configured and/or the method may include calculating theactivation (amplitude of the spontaneous BOLD fMRI fluctuations overtime), correlation (frequency of the spontaneous BOLD fMRI fluctuationsover time), and/or covariation (correlation to activation of thespontaneous BOLD fMRI fluctuations over time) for the various brainnetworks and/or regions.

Systems, methods, and non-transitory computer-accessible medium havingstored thereon computer-executable instructions for provided herein fordetermining one or more target regions for TMS treatment of a patient.The systems, methods, and instructions may be configured to: receivefMRI data of a head of the patient; analyze the functional connection ofthe patient's brain through analysis of the fMRI data by determiningchanges in any combination of a first fluctuation in amplitude of thefMRI imaging data; a second fluctuation in frequency of the fMRI data,or a third fluctuation in frequency relative to amplitude of the fMRIdata; and determine one or more target regions for TMS treatment of apatient based on the determination of any combination of the firstfluctuation, the second fluctuation, or the third fluctuation.

The systems, methods, and instructions may be configured to compare thedetermination of any combination of the first fluctuation, the secondfluctuation, or the third fluctuation with measurements of a healthycontrol group matching one or more characteristics of the patient.Exemplary characteristics of a patient may comprise any combination ofgender, age, age range, ethnicity, weight, symptoms, diagnosis, priortreatment, response to prior treatment, genetic factors, prior medicalconditions, symptoms and/or diagnosis of the patient. The systems,methods, and instructions may analyze the functional connection of apatient's brain by determining the activation, correlation, andcovariation matrices between different brain networks of the patient'sbrain. The systems, methods, and instructions may analyze the functionalconnection of a patient's brain by determining activation, correlation,and covariation matrices between regions within networks.

The systems, methods, and instructions may analyze the functionalconnection of a patient's brain by determining the activation,correlation, and covariation matrices between different brain regionswithin a network and all other regions within the same network. Thesystems, methods, and instructions may analyze the functional connectionof a patient's brain by determining the activation, correlation, andcovariation matrices between different brain regions of differentnetworks. The systems, methods, and instructions may select a firstplurality of regions having a low change in amplitude, frequency, andfrequency relative to amplitude with the larger number of brain networksor regions. The systems, methods, and instructions may select a secondplurality of regions having a high change in amplitude, frequency, andfrequency to amplitude with a large number of brain networks or regions.

The systems, methods, and instructions may compare the first pluralityof regions and second plurality of regions with regions from a healthycontrol group matching the patient's characteristics, and selecting theregions above a first threshold value and below a second threshold valueto create a potential target group of regions for treatment. Thesystems, methods, and instructions may compare the potential targetgroup of regions for treatment with a second control group matching thepatient's symptoms or diagnosis and determining a subset from thepotential target group of regions by determining which of the regionshas the greatest change in fMRI data from pre-treatment topost-treatment from the second control group. The systems, methods, andinstructions may comprise comparisons of brain regions and networksoutside of the DLPhFC.

The systems, methods, and instructions may comprise selecting more thanone target location for treatment based on the analysis. The systems,methods, and instructions may include analyzing frequency changes aswell as amplitude in determining target locations for treatment. Thesystems, methods, and instructions may include early stage diagnosis andtreatment options for detecting areas in jeopardy of brain damage beforestructure detection is evident.

The systems, methods, and instructions may be used to determine regionsof a patient's brain that is negatively correlated with a large numberof other areas of the brain. Using the fMRI to compare functionalconnections of brain areas, the systems, methods, and instructions mayprovide early detection of future brain injury by identifying areas ofthe brain that are functionally disconnected, but which have not yetexperienced neurological death or physical deterioration.

Systems, methods, and non-transitory computer-accessible medium havingstored thereon computer-executable instructions for determining one ormore target regions for TMS treatment of a patient may be configured to:receive MM data of a head of the patient; analyze the structuralconnection of the patient's brain through analysis of the MRI data bygenerating a brain structural connectivity matrix, constructed based onwhite matter tractography from the whole brain. In an embodiment,structural data is used to run structural connectivity analysis(tractography). The structural data may be T1w/T2w. T1-weighted (T1w)and T2-weighted (T2w) are MRI sequence weighted scans in which T1w MMmay enhance the signal of fatty tissue and suppresses the signal of thewater, while T2w MM may enhance the signal of the water. The structuralconnectivity matrix may comprise a comparison of the strength of thewhite matter connection between each combination of two parcels within aplurality of parcels. The strength of the white matter connection may bemade using diffusion MRI streamlines tractography. Using voxel-specificdirectional diffusion information from diffusion-weighted MM (dMRI),computational tractography produces three-dimensional trajectoriesthrough the white matter within the MM volume that are calledstreamlines. The connections between the corresponding regions ofinterest (ROIs) may be quantified as the number of streamlinestherebetween. The systems and methods may include selecting targetlocations for stimulation may include choosing regions with a highernumber of white matter connections as compared to other regions. Thesystems and methods may include selecting target locations forstimulation may include choosing regions with the highest number ofwhite matter connections from the pool of targets based on fMRIfunctional connectivity.

The systems and methods herein may be used to determine target regionsfor stimulation using any combination of functional connectivity and/orstructural connectivity. The structural connectivity and functionalconnectivity analysis may be used separately or in combination. Whenused in combination, the analysis may be run sequentially such that afirst set of target regions are identified using a first analysis(whether structural or functional connectivity) and then focused usinganother analysis (the other of the functional or structuralconnectivity) of those first set of target regions. In this instance,the another analysis may be used on an outcome from the first analysisto further refine the results from the first analysis to create a targetset of regions for stimulation treatment. When used in combination, theanalysis may be run concurrently such that a first set of target regionsare identified using a first analysis (whether structure or functionalconnectivity) and a second set of target regions are identified using asecond analysis (whether functional or structure connectivity). Thefinal determination of targets for stimulation may be a combination ofregions from either or both results from the first analysis and secondanalysis.

Embodiments described herein may use more anatomically specific modelsof brain connectivity (functional and/or structural) that can beconstructed for individual patients for targeted stimulation.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and constitutepart of this specification, are illustrative of particular embodimentsof the present disclosure and do not limit the scope of the presentdisclosure. The drawings are not to scale and are intended for use inconjunction with the explanations in the following detailed description.

FIG. 1 illustrates a process diagram of embodiments of the method usingthe network guided stimulation approach.

FIG. 2 is an illustrative working model explanation of the methodsdescribed herein.

With respect to FIGS. 3A-8B, figures with an “A” provide exemplary flowdiagrams according to algorithms described herein, while figures with a“B” illustrate representative brain segmentations to explain the flowdiagram. Accordingly,

FIGS. 3A-3B illustrate exemplary analysis for determining covariationfor each network within the brain of a patient.

FIGS. 4A-4B illustrate exemplary analysis for determining covariationfor each region within the brain.

FIGS. 5A-5B illustrate exemplary analysis for comparing covariationbetween networks within the brain.

FIGS. 6A-6B illustrate exemplary analysis for comparing covariation ofeach region against each external network from the network containingthe region being compared.

FIGS. 7A-7B illustrate exemplary analysis for comparing covariationbetween regions within the same network.

FIGS. 8A-8B illustrate exemplary analysis for comparing covariation ofregions within one network to regions within other networks. The systemsand methods may use any combination of the analysis with respect to thedifferent covariation calculations and comparisons.

FIGS. 9A-9F illustrates different views of the fMRI brain scan takenfrom patients before receiving TMS treatment.

FIGS. 10A-10F illustrates different views of the fMRI brain scan fromthe patients of FIGS. 9A-9F after receiving TMS treatment.

FIGS. 11A-124D, 126A-D, and 128A-147D illustrate different views of fMRIscans of a patient before and after treatments and of control groups forcomparison according to exemplary embodiments of methods for identifyingtarget locations for stimulation treatment. The figures of the brainscans are separately identified with letters to identify different viewsfor the same brain scan having the same number.

FIGS. 11A-11B are a set of scans at baseline showing target treatmentareas at rSFC area STSdp;

FIGS. 12A-12B are a set of scans after fMRI guided TMS according tomethods herein; and

FIGS. 13A-13B are a set of scans of a healthy control group matching thesame age and sex as the patient.

FIGS. 14A-16B are a set of scans at baseline showing target treatmentareas at RSFC area TPO1;

FIGS. 15A-15B are scans after fMRI guided TMS according to methodsherein; and

FIG. 16A-16B are scans of a healthy control group matching the same ageand sex as the patient.

FIGS. 17A-17B are a set of scans at baseline showing target treatmentareas at RSFC area STV; FIGS. 18A-18B are scans after fMRI guided TMSaccording to methods herein; and

FIG. 19A-19B are scans of a healthy control group matching the same ageand sex as the patient.

FIGS. 20A-20B are a set of scans at baseline showing target treatmentareas at RSFC area SFL;

FIGS. 21A-21B are scans after fMRI guided TMS according to methodsherein; and

FIGS. 22A-22B are scans of a healthy control group matching the same ageand sex as the patient.

FIGS. 23A-23B are a set of scans at baseline showing target treatmentareas at RSFC area 55b;

FIGS. 24A-24B are scans after fMRI guided TMS according to methodsherein; and

FIGS. 25A-25B are of a healthy control group matching the same age andsex as the patient.

FIGS. 26A-26B are a set of scans at baseline showing target treatmentareas at RSFC area 44;

FIGS. 27A-27B are scans after fMRI guided TMS according to methodsherein; and

FIG. 28A-28B are of a healthy control group matching the same age andsex as the patient.

FIGS. 29A-29B are a set of scans at baseline showing target treatmentareas at RSFC area 45;

FIGS. 30A-30B are scans after fMRI guided TMS according to methodsherein; and

FIG. 31A-31B are of a healthy control group matching the same age andsex as the patient.

FIGS. 32A-32D are a set of scans at baseline showing target treatmentareas at RSFC area p9-46v;

FIGS. 33A-33D are scans after fMRI guided TMS according to methodsherein; and

FIGS. 34A-34D are of a healthy control group matching the same age andsex as the patient.

FIGS. 35A-35D are a set of scans at baseline showing target treatmentareas at RSFC area STSdp;

FIGS. 36A-36D are scans after fMRI guided TMS according to methodsherein;

FIGS. 37A-37D are of a healthy control group matching the same age andsex as the patient.

FIGS. 38A-38D are a set of scans at baseline showing target treatmentareas at RSFC area TPOJ1;

FIGS. 39A-39D are scans after fMRI guided TMS according to methodsherein;

FIG. 40A-40D are of a healthy control group matching the same age andsex as the patient.

FIGS. 41A-41D are a set of scans at baseline showing target treatmentareas at RSFC area 46 left;

FIGS. 42A-42D are a set of scans after fMRI guided TMS according tomethods herein including a complete circuit based individual targetplan;

FIGS. 43A-43D are a set of scans after a conventional single treatmentof a targeted TMS treatment based on fMRI data comparing area 46 leftwithin the DLPFC only; and

FIGS. 44A-44D are a set of scans of control group matching the same ageand sex as the patient.

FIGS. 45A-45D are a set of scans at baseline showing target treatmentareas at RSFC area PF left;

FIGS. 46A-46D are a set of scans after fMRI guided TMS according tomethods herein including a complete circuit based individual targetplan;

FIGS. 47A-47D are a set of scans after a conventional single treatmentof a targeted TMS treatment based on fMRI data comparing area PF leftwithin the DLPFC only; and

FIGS. 48-48D are a set of scans of a health control group matching thesame age and sex as the patient.

FIGS. 49A-49D are a set of scans at baseline showing target treatmentareas at RSFC area 46 right;

FIGS. 50A-50D are a set of scans after fMRI guided TMS according tomethods herein including a complete circuit based individual targetplan;

FIGS. 51A-51D are a set of scans after a conventional single treatmentof a targeted TMS treatment based on fMRI data comparing area 46 rightwithin the DLPFC only; and

FIG. 52A-52D are a set of scans of a health control group matching thesame age and sex as the patient.

FIGS. 53A-53D are a set of scans at baseline showing target treatmentareas at RSFC area PF right;

FIGS. 54A-54D are a set of scans after fMRI guided TMS according tomethods herein including a complete circuit based individual targetplan;

FIGS. 55A-55D are a set of scans after a conventional single treatmentof a targeted TMS treatment based on fMRI data comparing area PF rightwithin the DLPFC only; and

FIGS. 56A-56D are a set of scans of a health control group matching thesame age and sex as the patient.

FIGS. 57A-57D are a set of scans at baseline showing target treatmentareas at RSFC area STGa;

FIGS. 58A-58D are scans after fMRI guided TMS according to methodsherein; and

FIGS. 59A-59D are of a healthy control group matching the same age andsex as the patient.

FIGS. 60A-60D at baseline showing target treatment areas at RSFC areaSTSda;

FIGS. 61A-61D are a set of scans after fMRI guided TMS according tomethods herein; and

FIGS. 62A-62D are of a healthy control group matching the same age andsex as the patient.

FIGS. 63A-63D are a set of scans at baseline showing target treatmentareas at RSFC area STSva;

FIGS. 64A-64D are scans after fMRI guided TMS according to methodsherein; and

FIGS. 65A-65D are of a healthy control group matching the same age andsex as the patient.

FIGS. 66A-66D are a set of scans at baseline showing target treatmentareas at RSFC area STSdp;

FIGS. 67A-67D are scans after fMRI guided TMS according to methodsherein; and

FIGS. 68A-68D are of a healthy control group matching the same age andsex as the patient.

FIGS. 69A-69D are a set of scans at baseline showing target treatmentareas at RSFC area STSvp;

FIGS. 70A-70D are scans after fMRI guided TMS according to methodsherein; and

FIGS. 71A-71D are of a healthy control group matching the same age andsex as the patient.

FIGS. 72A-72D are a set of scans at baseline showing target treatmentareas at RSFC area FOP5;

FIGS. 73A-73D are scans after fMRI guided TMS according to methodsherein; and

FIGS. 74A-74D are of a healthy control group matching the same age andsex as the patient.

FIGS. 75A-75D are a set of scans at baseline showing target treatmentareas at RSFC area FOP4;

FIGS. 76A-76D are scans after fMRI guided TMS according to methodsherein; and

FIGS. 77A-77D are of a healthy control group matching the same age andsex as the patient.

FIGS. 78A-78D are a set of scans at baseline showing target treatmentareas at RSFC area FOP3;

FIG. 79A-79D are scans after fMRI guided TMS according to methodsherein; and

FIGS. 80A-80D are of a healthy control group matching the same age andsex as the patient.

FIGS. 81A-83D are a set of scans at baseline showing target treatmentareas at RSFC area FOP2;

FIGS. 82A-82D are scans after fMRI guided TMS according to methodsherein; and

FIGS. 83A-83D are of a healthy control group matching the same age andsex as the patient.

FIGS. 84A-84D are a set of scans at baseline showing target treatmentareas at RSFC area TE1a;

FIGS. 85A-85D are scans after fMRI guided TMS according to methodsherein; and

FIGS. 86A-86D are of a healthy control group matching the same age andsex as the patient.

FIGS. 87A-87D are a set of scans at baseline showing target treatmentareas at RSFC area TE1m;

FIGS. 88A-88D are scans after fMRI guided TMS according to methodsherein; and

FIGS. 89A-89D are of a healthy control group matching the same age andsex as the patient.

FIGS. 90A-90D are a set of scans at baseline showing target treatmentareas at RSFC area TE1p;

FIGS. 91A-91D are scans after fMRI guided TMS according to methodsherein; and

FIGS. 92A-92D are of a healthy control group matching the same age andsex as the patient.

FIGS. 93A-93D are a set of scans at baseline showing target treatmentareas at RSFC area PHT;

FIGS. 94A-94D are scans after fMRI guided TMS according to methodsherein; and

FIGS. 95A-95D are of a healthy control group matching the same age andsex as the patient.

FIGS. 96A-96D are a set of scans at baseline showing target treatmentareas at RSFC area TE2a;

FIGS. 97A-97D are scans after fMRI guided TMS according to methodsherein; and

FIGS. 98A-98D are of a healthy control group matching the same age andsex as the patient.

FIGS. 99A-99D are a set of scans at baseline showing the inferiorparietal cortex, area PGp, left having a high negativecorrelation/covariation with a large group of brain regions;

FIGS. 100A-100D are a set of scans after treatment according to methodsherein; and

FIGS. 101A-101D are a set of scans of an exemplary control group basedon age and sex.

FIGS. 102A-102D are a set of scans at baseline showing a high negativecorrelation/covariation with a large group of brain regions at theparieto-occipital sulcus area (are POS1, left);

FIGS. 103A-103D are a set of scans after treatment according to methodsherein; and

FIGS. 104A-104D are a set of scans of an exemplary control group basedon age and sex according to embodiments described herein.

FIGS. 105A-105D are a set of scans at baseline showing a frontal polarcortex (area a47r, right), having a high negativecorrelation/covariation with a large group of brain regions usingmethods described herein.

FIGS. 106A-106D are scans after treatment according to methods herein;and

FIGS. 107A-107D are of an exemplary control group based on age and sex.

FIGS. 108A-108D are a set of scans at baseline showing targetstimulation at parieto-occiptial sulcus area (area POS1, right) having ahigh negative correlation/covariation with a large group of brainregions;

FIGS. 109A-109D are after treatment according to methods herein; and

FIGS. 110A-110D are a set of scans of an exemplary control group basedon age and sex.

FIGS. 111A-111D are a set of scans at baseline showing dorsolateralprefrontal cortex (area 9p, right) having a high negativecorrelation/covariation with a large group of brain regions;

FIGS. 112A-112D are a set of scans after treatment according to methodsherein; and

FIGS. 113A-113D are a set of scans of an exemplary control group basedon age and sex.

FIGS. 114A-114D are a set of scans at baseline showing a superiorparietal cortex (area 7PC, right) having a high negativecorrelation/covariation with a large group of brain regions;

FIGS. 115A-115D are a set of scans after treatment according to methodsherein; and

FIGS. 116A-116D are a set of scans of a control group based on age andsex.

FIGS. 117A-117D are a set of scans at baseline showing exemplaryinferior frontal ocrtex (area 45, right) having a high negativecorrelation/covariation with a large group of brain regions;

FIGS. 118A-118D are scans after treatment according to methods describedherein; and

FIGS. 119A-119D are a set of scans of an exemplary control group basedon age and sex.

FIGS. 120A-120D are a set of scans at baseline showing an exemplaryfrontal polar cortex (area 10pp, right) having a high negativecorrelation/covariation with a large group of brain regions; and

FIGS. 121A-121B are scans after treatment according to methods herein.

FIGS. 122A-122D are a set of scans at baseline showing an exemplaryfrontal polar cortex (area TE1m, right) having a high negativecorrelation/covariation with a large group of brain regions; and

FIGS. 123A-123D are after treatment according to methods herein.

FIGS. 124A-127 are brain scans for a patient having experienced astroke.

FIGS. 124A-124D and 126A-126D are from an fMRI brain scan for patientsexperiencing a stroke showing close to zero functional connectivity.

FIGS. 125 and 127 illustrate the corresponding image to show thecorresponding structural degradation of the brain areas injured by thestroke.

FIGS. 128A-147D are fMRI brain scans of patients before or aftertreatment according to exemplary embodiments described herein, or ofcontrol persons.

FIG. 128A-128D are a set of scans at baseline (pre-treatment of apatient) to illustrates an exemplary RSFC area p9-46v identified aspotential targets for stimulation; and

FIGS. 129A-129D are a set of scans after fMRI guided TMS according tomethods herein.

FIGS. 130A-130D are a set of scans at baseline to illustrate anexemplary RSFC area, right hemisphere, dorsolateral prefrontal, areaa9-46v, right as potential targets for stimulation; and

FIGS. 131A-131D are a set of scans after fMRI guided TMS according tomethods herein.

FIGS. 132A-132D are a set of scans at baseline to illustrate anexemplary RSFC area right hemisphere, anterior cingulate, area d32,right, as potential targets for stimulation.

FIG. 133A-133D are a set of scans after fMRI guided TMS according tomethods herein.

FIGS. 134A-134D are a set of scans at baseline to show an exemplary RSFCarea right hemisphere, anterior cingulate, area a32pr, right, aspotential targets for stimulation;

FIGS. 135A-135D are a set of scans after fMRI guided TMS according tomethods herein.

FIGS. 136A-136D are a set of scans at baseline to show an exemplary RSFCarea right hemisphere, anterior cingulate, area 9m, right, as potentialtargets for stimulation;

FIGS. 137A-137D are a set of scans after fMRI guided TMS according tomethods herein.

FIGS. 138A-138D are a set of scans at baseline to show an exemplary RSFCarea left hemisphere parieto-occipital sulcus areas, area POS2, left,showing target locations for stimulation;

FIGS. 139A-139D are scans after fMRI guided TMS according to methodsherein.

FIGS. 140A-140D are a set of scans at baseline to show potential targetsfor stimulation at an RSFC area, right hemisphere, parieto-occipitalsulcus areas, area POS2, right;

FIGS. 141A-141D are a set of scans after fMRI guided TMS according tomethods herein.

FIGS. 142A-142D are a set of scans at baseline to show potential targetsfor stimulation at RSFC area right hemisphere, parieto-occipital sulcusareas, area POS1, right;

FIGS. 143A-143D are a set of scans after fMRI guided TMS according tomethods herein.

FIGS. 144A-144D are a set of scans at baseline to show potential targetsfor stimulation at RSFC area right hemisphere, superior parietal lobuleareas, area 7Pm, right;

FIGS. 145-145D are a set of scans after fMRI guided TMS according tomethods herein.

FIGS. 146A146D are a set of scans at baseline to show potential targetsfor stimulation at RSFC area right hemisphere, superior parietal lobuleareas, area 7Am, right;

FIGS. 147A-147D are a set of scans after fMRI guided TMS according tomethods herein.

DETAILED DESCRIPTION

The following discussion omits or only briefly describes conventionalfeatures of the disclosed technology that are apparent to those skilledin the art. Reference to various embodiments does not limit the scope ofthe claims attached hereto. Additionally, any examples set forth in thisspecification are intended to be non-limiting and merely set forth someof the many possible embodiments for the appended claims. Further,particular features described herein can be used in combination withother described features in each of the various possible combinationsand permutations. A person of ordinary skill in the art would know howto use the instant invention, in combination with routine experiments,to achieve other outcomes not specifically disclosed in the examples orthe embodiments.

Unless otherwise specifically defined herein, all terms are to be giventheir broadest possible interpretation including meanings implied fromthe specification as well as meanings understood by those skilled in theart and/or as defined in dictionaries, treatises, etc. Unless definedotherwise, all technical and scientific terms used herein have the samemeaning as commonly understood by one of ordinary skill in the art inthe field of the disclosed technology. It must also be noted that, asused in the specification and the appended claims, the singular forms“a,” “an” and “the” include plural referents unless otherwise specified,and that the terms “includes” and/or “including,” when used in thisspecification, specify the presence of stated features, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, steps, operations, elements, components, and/or groupsthereof. Additionally, methods, equipment, and materials similar orequivalent to those described herein can also be used in the practice ortesting of the disclosed technology.

Because a patient's mind is the outcome of electrical signals generatedall around the brain upon command, the way that the brain learns tocarry out tasks is by coordinating these electrical signals and bunchingthem together based on their frequencies. As such, if the oscillation ofthe electrical signals falls out of pace with other members of thenetwork, the network loses its ability to properly function. Dependingon which network is malfunctioning, the individual experiences theoutcome through adverse cognition, emotions and behavior, the classictrademarks of psychiatric disorders.

The system and methods for network guided stimulation provided hereininclude a personalized approach for correcting the connections of apatient's brain. Embodiments of region and/or network guided stimulationuse fMRI as the revolutionary treatment's guidance to restore patientwellness. Because brain illnesses do not follow a one-size-fits alltreatment approach, the disclosed systems and methods may includeadvanced fMRI technology to treat complex brain illnesses with anadvanced precise approach using stimulation, such as, for example, TMS.Individual variations in brain organization may require stimulation tobe applied to slightly different locations in different individuals.Thus, an important goal of stimulation therapy is to guide stimulationtargeting on a personalized basis in order to improve consistency oftargeting across individuals. Although descried herein as a treatmentmethod using TMS, other stimulation methods may also be used, such as,for example ultrasound and/or electrical.

The systems and methods for network guided stimulation, such as TMS,described herein include “Network/Circuit” detection that are used astargets for applying stimulation (such as TMS) therapy. Circuits ornetworks are detected based on activation and connectivity as determinedbased on images from the fMRI technology. The systems and methods ofnetwork guided stimulation (such as TMS) may: (1) Use network guidedstimulation for targeting specific circuits instead of single regions ofthe patient's brain; (2) Use network guided stimulation herein ontraumatic brain injuries and other neurological conditions, such asstroke, dementias, etc., and not just psychiatric conditions; (3) Usenetwork guided stimulation on psychiatric conditions with network-basedtargeting post-treatments having improvements over single regionapplication of TMS.

FIG. 1 illustrates an process diagram of a method 100, 150 using thenetwork guided stimulation approaches. The systems and methods may useany combination of functional 100 and structural 150 connections betweenareas of the brain (including regions and networks) to determinepersonalized locations for stimulation treatment. As illustrated, thesystems and methods may include acquiring high-resolution fMRI images ofthe patient's brain, step 102; acquiring dMRI images of the patient'sbrain, step 152; analyzing the fMRI images to identify the circuits ofthe patient's brain to be treated from the analyzed fMRI images, step104; analyzing the dMRI images to identify white matter tractography,step 154; computing the appropriate stimulation based on the analyzedfMRI and/or dMRI images, step 106; and applying appropriate stimulationto the patient's brain based on the appropriate stimulation, step 108.

FIG. 1 , step 102, the method 100 may include acquiring images foranalysis. The images may be of the patient's brain. The images mayinclude acquiring high-resolution fMRI brain images, which allowdetection for each individual of a unique pattern of active brain areas(Nodes) and inter-Node connections (Circuits) that comprise a variety ofnetworks, each with different functions. Embodiments may also oralternatively use dMRI images to make assessments of target locationsfor stimulation treatment.

The systems and methods may include receiving a detailed medical andpsychiatric history if the patient. The method may also include creatinga detailed map of the brain where the problems reside. The methods mayinclude seating a patient comfortably in a chair similar to dentalchairs and fitting with a sensor cap. A tracker (sensors) attached to aband is placed around the patient's head. The tracker can then receivesignals from the patient. Embodiments include obtaining a fMRI, which isa radiological scan that shows how blood is flowing through the brain inreal-time. If blood flow to a particular brain region is too high or lowcompared to normal it will be correlated to the clinical presentation.Areas of abnormal blood flow have been determined to represent poorlyfunctioning regions and their associated dysfunctions.

The system and methods may include obtaining a resting state fMRI(rsfMRI). rsfMRI is an imaging technique that can detect functionallylinked brain regions. If multiple fast MRI images are acquired, then amap of fMRI can be constructed by finding the temporal fluctuations inthe time series of each brain region (voxel). This is achieved by fMRI'ssensitivity to spontaneous BOLD contrast fluctuations. Since the BOLDsignal between different brain regions that work together is temporallycorrelated when the subject is at rest (the subject is presented with nospecific stimulus or task) fMRI can reveal deficiencies of various brainnetworks.

The systems and methods may include receiving fMRI images. The systemmay be configured with a Magnetic Resonance Imaging machine. The MMmachine may be configured to detect changes associated with blood flowin different parts of the brain. The system may be communication withthe MM machine to take and receive the fMRI images. The system andmethods may also be configured to receive fMRI images that arepreviously taken or separately taken before the TMS treatment. In thiscase, the system may be configured to communicate, such as through theinternet, e-mail, electronic transfer or file share, electronic transferthrough memory devices, or other methods in order to receive electronicfiles having the fMRI images taken with an MRI device before the TMSprocedure.

FIG. 1 , step 104, the method 100 may include analyzing the fMRI imagesto determine target locations for treatment sites. Resting-statenetworks (RSN) are made of a set of brain regions with coherentspontaneous fluctuations in activity. fMRI allows us to explore thebrain's functional organization and to examine its differences withnormal controls to find malfunctions in neurological or psychiatricdiseases. Functional connectivity (FC) may then be calculated by findingstatistical dependencies between different regions (voxels). ForGaussian distribution of data, second-order dependencies (i.e.,covariances or correlations) are found to build functional connectivitymaps.

The system and methos may include determining what loci (locations) ofthe brain are poorly functioning and correlate it with verysophisticated software. Based on the poor functional connections of thebrain, a treatment plan may be devised based on brain circuit(functional), wiring and realigning the target pathways with magneticbeams. After reviewing thousands of the individual's fMRI images, Thesystems and methods herein may identify the unique pattern of networkanomalies, which would include abnormal node function and/or abnormalinternode and intranode connections. Embodiments may identify andextract amplitude and frequency of brain activity within and/or betweenbrain networks and/or brain regions in order to analyze connectivitybetween circuits. The systems and methods may also include algorithmsfor identifying and extracting amplitude and frequency of brain activitywithin and between brain networks and/or regions for precise andpersonalized stimulation treatment, such as TMS treatment.

The systems and methods may use BOLD imaging to measure brain activity.The BOLD images may be subdivided into regions and networks to makedifferent comparisons of the fMRI data of the patient and/or between thepatient and one or more healthy and/or control groups. The regions ofthe brain may be divided based on recognized regions, such as, forexample, the Brodmann areas. Other subdivisions of the brain may also bemade, such as, for example, based on different functional, connective,and/or developmental criteria. The region may be defined by the Glasseratlas. Regions may be thought of as broader brain areas, e.g., the DLPFCmay be a brain region, which is divided based on multimodal techniquesinto smaller brain areas such as e.g., a9-46v, 9-46, etc. Glasser usesthe word area to refer to these subdivisions within regions. Glasserdivides the brain in 22 brain regions (bilateral) and 360 brain areastotal. The networks of the brain may be identified as collections ofregions. The collection of regions may be based on functionalconnectivity by statistical analysis of the fMRI BOLD signal of thepatient and/or based on recognized networks of healthy patients.Networks may also be identified through other recording methods such asEEG, PET, or MEG. Networks may be defined as a group of regions of thebrain that are functionally connected. Functional connections may befound using algorithms such as cluster analysis, spatial independentcomponent analysis (ICA), seed based, and others. Networks may bedefined based on the resting state of the individual and may includeresting state networks (RSN). Networks may include, for example, anycombination of medial frontoparietal, midcingulo-insular, dorsalfrontoparietal, lateral frontoparietal, pericentral, occipital, limbic,auditory, cerebellar, spatial attention, language, lateral visual,temporal, visual perception, left/right executive. The networks aredefined by the Cole-Anticevic atlas. A network may be a group of brainareas (that may be different from brain regions)(or subset of areaswithin regions) that are interconnected (structurally and/orfunctionally). Exemplary embodiments may be in terms of identifyingregions for TMS target treatment locations, and/or using brain regionsin comparisons to identify the target regions. Embodiments may also useareas for target TMS treatment and/or using areas for determining thetarget locations. Therefore, embodiments may be applied to areas asdescribed herein with respect to regions.

The systems and methods may be used to target individual brain areasbased on functional connectivity. Embodiments may use the concept of acircuit to refer to multiple brain areas from the same and/or differentnetworks in order to target the circuits to restore the integrity of anetwork or the interaction between networks. Comparisons of the BOLDimages may be made by comparing different regions and networks of thepatient's brain. The regions and networks may be analyzed by comparingthe activation, correlation and covariation matrices of the differentBOLD regions and/or networks.

The systems and methods may be used to target individual brain areasbased on structural connectivity. Embodiments may use dMRT tractography.Structural connectomes may use dMRT tractography in addition to or incombination with those derived through other imaging modalities (e.g.,functional MRI) to study and identify underlying white matter tracts ofthe cortical regions shown as faulty in the functional connectivity maps(i.e., based on activation, covariation, and/or correlation). Functionaldata may be used initially to select certain brain regions as potentialstimulation targets, and then structural data analyses may be used torefine and select priority targets from the potential stimulationtargets. fMRI data may be used to guide structural analyses, such as foruse when psychiatric disorders may occur with unknown concurrence ofstroke, traumatic brain injury, neurodevelopmental or neurodegenerativedisorders. Structural data may be used to guide functional analysis,such as when stroke, traumatic brain injury, neurodevelopment orneurodegenerative disorders are present in the patient. The analysis ofstructural connectivity may be used alone or in combination with theanalysis of functional connectivity.

If functional analysis is used to guide structural analysis, the processmay include: (1a) use fMRI data to construct the activation, correlationand covariation matrixes and select the targets with stronger (positiveand/or negative) activation/connectivity values with a larger group ofbrain regions and/or (1b) use fMRI data to construct the activation,correlation, and/or covariation matrixes and select regions in withweaker (close to o) activation to connectivity values with a largergroup of brain regions; and (2) use structural data to construct thestructural connectivity matrix and select regions from 1(a)/1(b) withhigher number of white matter connections.

If structural analysis is used to guide functional analysis, the processmay include: (1) use structural data to construct the structuralconnectivity matrix and select regions with lower number of white matterconnections; and (2a) use fMRI data to construct the activation,correlation, and/or covariation matrixes and select regions from (1)with stronger (positive and/or negative) activation to connectivityvalues (activation/connectivity) with a larger group of brain regions;and/or (2b) use fMRI data to construct the activation, correlation andcovariation matrixes and select regions from (1) with weaker (close to0) activation to connectivity (activation/connectivity) values with alarger group of brain regions.

The structural analysis may start with acquiring dMRI images of thepatient at step 152. The dMRI images are analysed to construct brainstructural connectivity matrix, at step 154. The target locations forstimulation may be determined based from the structural connectivitymatrix of step 154. The process may proceed with computing stimulationparameters and applying stimulation from steps 106 and 108.

Using the results from the functional connectivity based analysis forselecting a first set of potential targets, the structural connectivitymay be used to identify priority targets and/or additional targets byselecting a set from within and/or in addition to the first set ofpotential targets to define a set of targets for stimulation treatment.

fMRI guided stimulation treatments may be used to show percentageincrease from baseline on the following whole brain measures: totalcortical gray matter volume and white surface total area. The structuralanalysis may be used in combination with the functional analysis forpatients experiencing neurodevelopmental disorders, such as, forexample, autism spectrum disorders, and neurodegenerative diseases, suchas, for example, dementias and Alzheimer's disease. Embodiments may usestructural analysis for disorders or conditions of a patient in whichstructural changes occur within the brain. Embodiments may usestructural analysis as additional processing steps to identify targetlocations for stimulus, such as TMS treatments, based on structuralconnectivity.

At step 152, the process may include acquiring dMRI images. Thestructural processing steps use dMRI tractography. The structuralconnectome constructed using dMRT tractography (alone or in addition tothose derived through other imaging modalities, such as fMRI describedherein) may be used study underlying shite matter tracts of the corticalregions shown as fault in the functional connectivity maps, such asbased on activation, covariation, and correlation.

At step 154, the process may include analyzing dMRT images to constructbrain structural connectivity matrix. A brain structural connectivitymatrix may be generated based on white matter tractography from thewhole brain. A matrix is made of rows and columns representing braingray matter regions of interest (ROI) (parcels). Exemplary parcellationmay be brain gray matter parcellation based on Glasser, and the value inan element of the matrix is the strength of the white matter connectionbetween the two corresponding ROIs, quantified as the number ofstreamlines. Using voxel-specific directional diffusion information fromdiffusion-weighted MRI (dMRT), computational tractography producesthree-dimensional trajectories through the white matter within the MRIvolume that are called streamlines.

At step 156, the target locations for treatment are selected. Selectingtarget locations for stimulation may include choosing the regions ofinterest with the higher number of white matter connections. Theselection of the higher number of white matter connections may be fromthe pre-selected potential targets from an another analysis method, suchas, for example, the functional connectivity analysis. The selection ofthe higher number of white matter connections may be independent of anyother analysis and may provide additional target locations forstimulation. Selecting targets for stimulation includes choosing apredetermined number of target locations and/or selecting a number ofregions of interest in which the number of white matter connections areabove a threshold. Selecting targets for stimulation includes choosingregions of interest with the higher number of white matter connectionsform the pool of targets based on fMRI functional connectivity.

As illustrated, a system and method may include acquiring imaging, suchas dMRI images, at step 152. The system and method may then analyze thedMRI images to construct a brain structural connectivity matrix, at step156, in order to identify or select locations for stimulation at step156. The structural analysis may be performed before and/or after theanalysis of the functional analysis, steps 102 and 104. A first set ofidentified targets (such as from step 104) may be determined by a firstprocess, such as the functional analysis, and then those first set ofidentified targets may be used within the structural analysis such thata final selection of targets is based on a priority of the first set ofidentified targets based on the structural analysis. Alternatively, eachfunctional and structural analysis may be used separately so that atotal target set is determined based on a combination of analysisbetween the functional and structural analysis.

FIG. 1 , step 106, the method 100 may include selecting the appropriateTMS coil and the appropriate TMS stimulation, once the networks to betreated are identified. This computation may include the site ofstimulation, as well as the parameters which determine results ofstimulation, e.g., increasing or decreasing strength of nodes andnetworks. Parameters may include the strength of the magnetic pulse aswell as their frequency and the number of pulses administered during asession, and subsequent sessions, or any combination thereof. Byapplying repeated pulses (repetitive TMS) at high-frequencies (e.g., >5Hz), one can excite underlying cortical activity and low-frequency(e.g., <5 Hz) can result in inhibitory changes. The effects of TMS canpropagate beyond the stimulation site, through connectivity, impacting adistributed network of brain regions, making the use of resting statefunctional connectivity (rsFC) a powerful tool for assessing theconnectivity, and guiding the optimal coil position with regard to thetargeted area.

In determining the intensity of the pulse, the systems and methods mayuse a pulse with intensity at 100-120% above the patient motor threshold(MT). Depending on the coil being used, Theta Burst stimulation may beapplied in 50 Hz triplet bursts five times per second. Embodiments mayuse an intermittent Theta Burst Stimulation (iTBS), which means that thestimulation can be delivered in a cycle of approximately 2 seconds onand 8 seconds off over a period of 3 minutes. During a typicalstimulation session, the patient may receive a total of 600 pulses and200 bursts. This treatment is known to increase neuronal firing in agiven region, and as a result, increases brain activity and functionalconnectivity in the target region modulating the neural circuit.

Inhibitory Theta Burst stimulation may be applied in 50 Hz tripletbursts five times per second. Continuous Theta Burst Stimulation (cTBS)may be used, which means that the stimulation is delivered continuouslyover a period of about 40 seconds for a total of 600 pulses. Thisprotocol may be used to decrease neuronal firing which results indecreased regional brain activity and functional connectivity in thebrain regions that need to be slowed down, modulating the neuralcircuit. FIG. 1 , step 108, the method 100 may include stimulating thepatient according to the parameters computed at step 106. The system andmethod for stimulating a patient may include using a TMS device and/ornavigation system. Use of a navigation system may improve theappropriate placement of the stimulator at the initial and followingtreatment sessions. Other stimulation methods may also be usedincluding, for example ultrasonic and/or electric stimulation.

Methods and systems are provided for determining networks and regionswithin a patient's brain for administering TMS treatments. Althoughexplained herein in terms of TMS treatment, exemplary embodiments arenot so limited. In exemplary embodiments, the treatment may also oralternatively include low intensity focused ultrasound (LIFUS). In anexemplary embodiment, if the resulting brain region and/or network fortreatment is identified as a cortical brain circuit, then the locationmay be stimulated with TMS. If the brain region and/or network fortreatment is identified as a subcortical brain circuit, then thelocation may be stimulated with LIFUS. Other protocols and treatmentsmay also be used, such as, for example electrical stimulation.

TMS is a non-invasive (does not enter your body) brain stimulationtechnique that is used to change brain activity and correct abnormalactivity due to illness, using a magnetic field. This magnetic field canpass through the skull to the patient's brain, and induce an electriccurrent at the site of stimulation (focal). TMS can modulate theresting-state activity of the brain and fine-tune DMN plasticity; thedirection (increase or decrease in activity) and the extent of thismodulation depends on designing a specific rTMS protocol for eachindividual patient's brain activity. TMS involves the use of a magneticcoil placed over the top side of the head that sends magnetic pulsesthrough the skull (cranium) and into the brain.

The disclosure provided herein may provide different distinct aspectsover conventional systems, such as, for example: (1) distinctions andpersonalization of each patient's brain, acknowledging that every brainis different; (2) treatment targets found from the images of thepatient's brain; and (3) treatment delivery with laser precision to themalfunctioning site.

Consistency is important; with each TMS treatment, the networks that arebeing “retrained” with the application of the TMS pulses preferably hasthe same treatment, at the same location for it to begin to normalizethe associated network connectivity. Therefore, precise location oftreatment cites is desirable for improving success and providinglonger-lasting wellness. Unlike Standard TMS, where a one-size-fits-allapproach is used to select and direct the TMS coil, the personalized TMSApproach may use the unchanging anatomical features of the individual'sface (landmarks) for registration, and real-time navigation to directthe treatment. This technology allows The systems and methods mayprovide the treatment precisely where it is needed for each treatment.The navigation system works based on optical (infrared) tracking,improving precision and accuracy. The accuracy of this opticalnavigation system is higher than other systems because the registrationand real-time navigation are only dependent on the tracker and probevisibility to the infrared camera.

The systems and methods herein may use a precision neuronavigationguidance system, where millimeters of precision are attainable. Theguidance system may use functional near infrared spectroscopy (fNIRS)with neuronavigation to deliver precise targeting for the TMS treatmentin relation to the identified brain network. The Near infraredspectroscopy may use infrared light delivered through optical fibers tothe scalp ad through the skull and into the brain. The infrared light isscatters or reflected by the brain tissue and blood. A secondary set ofoptical fibers on the scalp capture the infrared light as it exits thehead. By detecting changes in the concentration of oxygenated andde-oxygenated hemoglobin in the blood which has been shown by the fMRIstudies, a specific navigation map can be created of the head of thepatient in relation to the networks used to determine the target TMStreatment locations.

The systems and methods delicately aim a magnetic beam at nodes ofnetworks or circuits of the brain which are functionally disconnectedfrom the normal areas. Using brain mapping technology, embodimentsattempt to restore brain circuits to a healthier condition and alleviatethe underlying abnormalities. FIG. 2 illustrates a working modelexplanation of the methods described herein. The method 200 may have apatient obtain a Magnetic Resonance Image (MM) of their brain at an MMcenter at step 202. The method may then perform functional and/orstructural connectivity analysis on the fMRI images at step 212, andprovide an output at step 214.

As illustrated, and explained above, the system may receive MM data indifferent ways. The MRI information may be functional MRI (fMRI) and/ordiffusion MM (dMRI). fMRI is a type of MM that measures the changes inblood flow that occurs with brain activity. fMRI demonstrates regional,time-varying changes in time-varying changes in brain blood flow. dMRIis a type of MRI in which image contrast is based on the diffusion ofwater molecules in tissue. The MM scans may be obtained in differentways. For example, the system may simply generate an input into thefunctional connectivity analysis (using fMRI) and/or structuralconnectivity analysis (using dMRI) from the MM center. The transfer maybe through any method to communicate the information, including, withoutlimitation direct connection or communication through a network, such asan online platform. The system may be configured to communicate throughfile transfer protocols and/or prefer preprocessing on the image databefore using the MRI/fMRI/dMRI data as an input into the functionalconnectivity analysis at step 212. For example, the system may usepublic domain information such as the Human connectome MM Protocol fordata acquisition. Embodiments may use HCP Pipelines for datapre-processing. Embodiments may use Glasser and Cole-Anticevic atlases,or others as they are developed and made publicly available for use. Theparticipating sites 204 may use the respective or desired file transferprotocols 206 to perform the desired computational algorithms 208 topre-process the MM data before it is analyzed for functional and/orstructural connectivity at step 212.

At step 212, the MM (fMRI and/or dMRI) data may be received by thesystem as preprocessed according to the description herein, steps 204,206, 208.

The system may analyze the functional and/or structural connectionswithin the patient's brain. The pre-processed data may be used tocompute the whole-brain activation, correlation, and covariationmatrices in individual subjects. Determining the whole-brain activationincludes determining a change in amplitude over time of the fMRI data.Determining the whole-brain correlation includes determining the changein frequency of the fMRI data over time. Determining the whole-braincovariation includes determining the change in frequency relative to achange in amplitude over time. As used herein, the covariation isunderstood to be a ratio of one of the correlation or activation to theother of the activation or the correlation. Therefore, althoughgenerally described herein as the correlation to the activation,covariation is also understood to include the reciprocal thereof andremain within the scope of the instant disclosure and definition.

Embodiments may also or alternatively have access to available data forassessing or determining a healthy control group that may include age,gender, race, or other genetic information relevant to the diagnosis andtreatment in pathology and/or prevention. Pre-processed data fromhealthy controls along the age span and pre-post fMRI/TMS inneuropsychiatry may be used to compute average whole-brain activation,correlation, and covariation matrices of a health population.

For example, at 210, the MM center may provide fMRI information ofhealthy patients and provide patient information relevant to therelationship to the individual patient, such as age, gender, ethnicity,etc. The system may also retain patient information including fMRI data,patient information relevant to the relationship, TMS protocols, andpatient outcomes including responses to TMS treatments based on givenTMS target locations. This information may be stored in a database andprovided as an input 210 for the functional connectivity analysis ofstep 212.

The functional connectivity analysis performed herein includes receivingpre-processed fMRI data from the patient and a healthy control data sethaving metrics in relation to the patient. The metrics may include anycombination of age, gender, ethnicity, etc. The metrics in relation tothe patient may include characteristics of the healthy control groupthat are the same or within a given range of the characteristic of thepatient. For example, gender may be determined based on whether the sexof the patient is the same as that of someone from the healthy controlgroup. For an age, the healthy control group may be selective if theyhave an age within a range of that of the patient, such as the patientand the control group fall within pre-determined ranges (such as 5 or 10year increments), or within a pre-determined range from that of thepatient (such as 5 or 10 years older or younger than the patient). Theage ranges may be based on recognized developmental levels of the brain,such as adolescence, adulthood, or based on deteriorationstages/durations based on age or a given disease or condition, etc.

At step 212, the method includes generating whole brain activation,whole brain correlation and whole brain covariation matrices separatelyfor the patient data and healthy control data set having metrics inrelation to the patient. Therefore, the pre-processed patient fMRI datais used to compute whole-brain activation (change in amplitude overtime), correlation (change in frequency over time), and covariation(change in frequency relative to change in amplitude over time) for eachbrain region. Embodiments include receiving pre-processed data fromhealthy controls along any combination of metrics including age span,gender, ethnicity, etc. For the pre-processed data from healthycontrols, the fMRI data may be processed before and after (pre/post) TMStreatment. Embodiments include computing whole brain activation (changein amplitude over time), correlation (change in frequency over time),and covariation (change in frequency relative to change in amplitudeover time) for each brain region for each of the pre- and post-treatmentfMRI data sets of the healthy controls having metrics within a givenrange from the patient.

After obtaining the activation, correlation, and covariation matrices ofthe patient and the healthy control group, the matrices may be assessedin various ways of intra-subject analysis by comparing network coherenceand network interactions within portions of the patient's brain, and/orinter-subject analysis by comparing network coherence and interactionsbetween other subjects (the healthy control group and/or the group ofsubjects with same diagnosis) and the patient. The assessment ofactivation, correlation, and covariation matrices of the patient mayinclude a first inter-network comparison. In this instance, the methodmay include computing changes between the activation, correlation, andcovariation matrices between brain networks. Therefore, embodiments mayinclude computing a change in amplitude, frequency, frequency relativeto amplitude between, or any combination thereof for different brainnetworks to generate a network to network, inter-network comparison. Forexample, a first network may be compared against a second network, andthen separately to a third network, through the nth network. A secondnetwork may then be compared against a third network, then a fourthnetwork, and on through the nth network, and so on until each networkhas been compared against each other network.

The assessment of activation, correlation, and covariation matrices ofthe patient may include a first intra-network comparison. In thisinstance the method may include computing changes between theactivation, correlation, and covariation matrices between cortical andsubcortical regions within each network. Therefore, embodiments mayinclude computing a change in amplitude, frequency, frequency relativeto amplitude between, or any combination thereof for different brainregions within a network. Computing the changes between different brainregions within a network and all other members of the network combined(region to network) may be used to generate an intra-network comparison.For example, a first area of network may be compared against all membersof the network (including and/or excluding the first area) combined,then a second area of the network may be compared against all othermembers of the network (including and/or excluding the second area)combined, and so on until each of the areas of the network and allnetworks of the brain have been analyzed.

The assessment of activation, correlation, and covariation matrices ofthe patient may include a second intra-network comparison. In thisinstance the method may include computing changes between theactivation, correlation, and covariation matrices between cortical andsubcortical regions within each network, similar to the firstintra-network comparison. However, in this case, instead of the regionto network comparison, a region to region comparison may now be made.Embodiments may include computing a change in amplitude, frequency,frequency relative to amplitude between, or any combination thereof fordifferent brain regions within the same network. Embodiments may includecomputing a change in amplitude, frequency, frequency relative toamplitude between, or any combination thereof for different brainregions and other brain regions within a network (region to region) togenerate an intra-network analysis. For example, a first brain region ofa first brain network, may be compared against a second brain region ofthe first brain network, then to the third brain region of the firstbrain network, through the nth brain region of the first brain network.The second brain region of the first brain network may then be comparedagainst the third brain region of the first brain network and each otherregion through the nth region of the first brain network. The rest ofthe brain regions may thereafter be compared against all other brainregions not already compared within that brain network. The method maythen compare the next brain network, by comparing the first brain regionof the second brain network, with each of the other brain regions of thesecond brain network, until each brain region within each network iscompared against each other region of its same network.

The assessment of activation, correlation, and covariation matrices ofthe patient may include a second inter-network comparison, and a secondregion to region comparison. In this instance, however, the method mayinclude computing changes between the activation, correlation, andcovariation matrices between brain networks, similar to the firstinter-network comparison, but between different region of thosenetworks. Therefore, embodiments may include computing changes betweenthe activation, correlation, and covariation matrices between brainregions of different brain networks. Embodiments may include computing achange in amplitude, frequency, frequency relative to amplitude between,or any combination thereof for different regions of different brainnetworks to generate a region to region, inter-network analysis. Forexample, a first region of a first network may be compared against afirst region of a second network, and then separately to a second regionof the second network, through the nth region of the second network. Thefirst region may then be compared to the first region of a thirdnetwork, through all of the regions of the third network, and so onuntil the first region of the first region has been compared against allof the regions of all of the other networks outside of the firstnetwork. Therefore, when compared with the region to region,intra-network analysis, each region may be compared against each otherregion of the brain thereby providing an entire brain analysis.

Embodiments of the present disclosure may also or alternatively includedifferent comparisons of the patient against other groups, such as thehealthy control group or the group of subjects with same diagnosis thatmay or may not be selected based on relationships with metrics of thepatient, such as, for example, age, ethnicity, gender,condition/illness, treatment history, treatment efficacy, or anycombination thereof.

A statistical comparison may be made of the measurements of the patientwith the same measurements made for the healthy control. The healthycontrol group or the group of subjects with same diagnosis match thesame age, gender, diagnosis or symptoms as the patient. However, othercriteria of metrics may be made to select members of the healthy controlgroup from that of a larger control group database. For example, thepatient measurements may be compared against a healthy control groupmatching the same relationship metrics (such as having the same age andgender) as the patient and may include a comparison of any combinationof the activation, correlation, covariation assessments includingnetwork to network, inter-network analysis; region to network,intra-network analysis; region to region, intra-network analysis, and/orregion to region, inter-network analysis of the patient to the healthycontrol group.

The comparison of the patient with the healthy control group or thegroup of subjects with same diagnosis may be against one or moredifferent control groups based on one or more different relationshipmetrics. For example, the patient measurements may be compared against afirst healthy control group matching a first set of relationshipmetrics, such as age and gender, as the patient and may include acomparison of any combination of the activation, correlation,covariation assessments including network to network, inter-networkanalysis; region to network, intra-network analysis; region to region,intra-network analysis, and/or region to region, inter-network analysisof the patient to the first healthy control group. The patientmeasurements may also or alternatively be compared against a secondcontrol group matching a second set of relationship metrics, such as ageand diagnosis or symptoms, as the patient and may include a comparisonof any combination of the activation, correlation, covariationassessments including network to network, inter-network analysis; regionto network, intra-network analysis; region to region, intra-networkanalysis, and/or region to region, inter-network analysis of the patientto the second control group. Different combinations of relationshipmetrics for the control groups may be used and fall within the scope ofthe instant disclosure. For example, a first healthy control group maycomprise the same age and gender as the patient; a second control groupmay comprise the same age and gender as the patient, and may include anoverlapping identification of symptoms or diagnosis; a third controlgroup may comprise the same age and may include an overlappingidentification of symptoms or diagnosis; a fourth control group mayinclude only those with the same symptoms and/or diagnosis as thepatient. The inter-subject analysis may be based on a comparison of one,two, three, or more different control groups. The healthy control groupmay be individuals with no personal and/or family history ofneurological or psychiatric conditions.

The system may be configured to perform any combination of the followingfunctional connectivity analysis:

-   -   (1) Network to network, inter-network analysis by computing a        change in amplitude, frequency, and frequency relative to        amplitude between different brain networks of the same patient,        for each network of the patient;    -   (2) Region to network, intra-network analysis by computing a        change in amplitude, frequency, and frequency relative to        amplitude between brain regions with a network and all other        members of the network of the same patient, for each region of        the patient.    -   (3) Region to region, intra-network analysis by computing        changes in amplitude, frequency, and frequency relative to        amplitude between different brain regions within a network and        all other members within the network of the same patient, for        each region of the patient;    -   (4) Region to region, inter-network analysis by computing        changes in amplitude, frequency and frequency relative to        amplitude between different brain regions of different networks        of the same patient, for each region of the patient.    -   (5) Statistical comparison of measurements of any combination of        (1)-(4) from the patient compared to similar functional        connectivity analysis of a healthy control group comprising        individuals matching the same age and sex as the patient;    -   (6) Statistical comparison of measurements of any combination of        (1)-(4) from the patient compared to similar functional        connectivity analysis of a healthy control group comprising        individuals matching the same age, sex, and diagnosis and/or        symptoms as the patient.

After the various computations and comparisons of the inter-patientand/or intra-patient has occurred to assess functional connectivity ofthe patient's brain, the system may be configured to output recommendedcircuits for stimulation. The TMS protocol may also be customed asneeded based on the TMS protocols from the healthy control groups.Exemplary protocols of TMS may include administering TMS at frequenciesbetween 0 and 100 Hertz with amplitudes of 50 to 150 percent of anindividual's motor threshold.

The system may analyze the functional and/or structural connectionswithin the patient's brain. As illustrated in FIG. 1 , the structuralanalysis may be performed independent of the functional analysis and/ormay be performed in combination therewith. For example, functionalanalysis may be used to assess the patient and determine targettreatment locations in which steps 102, 104, 110, 106, and 108 areperformed. Another example, structural analysis may be used to assessthe patient and determine target locations in which steps 152, 154, 156,106, and 108 are performed. The functional and structural analysis mayalso be used in combination. The functional analysis may be performedfirst in which steps 102, 104, 110 are performed to identify a first setof potential target locations. Those first set of potential targetlocations may then be prioritized or analysed according to embodimentsof the structural analysis to identify the target locations from withinthe first set of potential target locations, in which steps 152, 154,and 156 are performed to determine the ultimate parameters at step 106and administer stimulation treatment at step 108. The processes may beswitched such that the structural analysis is performed first togenerate the first set of potential target locations that is then usedwithin the functional analysis in order to identify the target locationsfrom the first set of potential target locations for treatment. Theprocesses may be performed in parallel (whether simultaneously orsequentially) such that the one treatment process does not feed into theother, but the entire brain is analyzed according to each embodiment andthen the resulting target locations are prioritized together to identifyfinal target locations.

The system may be configured at 212 to analyse the structuralconnections of the brain. This may include acquiring dMRT images of thepatient from the MM center 202. The dMRI images are analysed toconstruct brain structural connectivity matrix. The target locations forstimulation may be determined based from the structural connectivitymatrix (Structural Connectivity (SC) analysis of algorithm 212). Theprocess may proceed with computing stimulation parameters as the outputat 214.

Using the results from the functional connectivity based analysis forselecting a first set of potential targets, the structural connectivitymay be used to identify priority targets and/or additional targets byselecting a set from within and/or in addition to the first set ofpotential targets to define a set of targets for stimulation treatment.

MRI guided stimulation treatments may be used to show percentageincrease from baseline on the following whole brain measures: totalcortical gray matter volume and white surface total area. The structuralanalysis may be used in combination with the functional analysis forpatients experiencing neurodevelopmental disorders, such as, forexample, autism spectrum disorders, and neurodegenerative diseases, suchas, for example, dementias and Alzheimer's disease. Embodiments may usestructural analysis for disorders or conditions of a patient in whichstructural changes occur within the brain. Structural analysis mayinclude additional processing steps to identify target locations forstimulus, such as TMS treatments, based on structural connectivity.

The system at 212 may include analyzing dMRI images to construct brainstructural connectivity matrix. A brain structural connectivity matrixmay be generated based on white matter tractography from the wholebrain. A matrix is made of rows and columns representing brain graymatter regions of interest (ROI) (parcels). Exemplary parcellation maybe brain gray matter parcellation based on Glasser, and the value in anelement of the matrix is the strength of the white matter connectionbetween the two corresponding ROIs, quantified as the number ofstreamlines. Using voxel-specific directional diffusion information fromdiffusion-weighted MRI (dMRI), computational tractography producesthree-dimensional trajectories through the white matter within the MMvolume that are called streamlines.

The structural connection of the patient's brain may be analysed throughMRI data to generate a brain structural connectivity matrix. MRI datamay be used to run structural connectivity analysis (tractography). Thestructural data is used to run structural connectivity analysis(tractography). The structural data may be T1w/T2w. T1-weighted (T1w)and T2-weighted (T2w) that are MM sequence weighted scans in which T1wMRI may enhance the signal of fatty tissue and suppresses the signal ofthe water, while T2w MRI may enhance the signal of the water. Thestructural data (such as, for example, T1w/T2w) is used for structuralconnectivity analyses (such as tractography) to study whole brainstructural architecture.

The system may be configured to output 214 the target locations fortreatment that are selected from the algorithm 212. Selecting targetlocations for stimulation may include choosing the regions of interestwith the higher number of white matter connections. The selection of thehigher number of white matter connections may be from the pre-selectedpotential targets from an another analysis method, such as, for example,the functional connectivity analysis. The selection of the higher numberof white matter connections may be independent of any other analysis andmay provide additional target locations for stimulation. Selectingtargets for stimulation includes choosing a predetermined number oftarget locations and/or selecting a number of regions of interest inwhich the number of white matter connections are above a threshold.Selecting targets for stimulation includes choosing regions of interestwith the higher number of white matter connections form the pool oftargets based on fMRI functional connectivity.

At step 214, the output may comprise a selection of regions foradministering TMS. In order to obtain the selection of regions, thesystem may be configured to analyze the various computations made todetermine functional and/or structural connectivity, the comparisonsperformed in step 212. The system may be configured to select the topthreshold percentage of brain regions that satisfy select conditions foreach step of the analysis performed in step 212 in which regions and/ornetworks of the fMRI data is analyzed to determine functionalconnectivity and/or for regions with higher number of white matterconnections to determine structural connectivity. The computations madefor determining functional connectivity may include, as an example, anycombination of (1) network to network, inter-network analysis, (2)region to network, intra-network analysis; (3) region to region,intra-network analysis; (4) region to region, inter-network analysis;and (5) One or more statistical comparisons of measurements of anycombination of (1) through (4), above, from the patient compared tosimilar functional connectivity analysis of a control group, in whichthe control group may be selected on one or more combination ofrelationship metrics, such as, for example age, gender, symptoms,diagnosis, ethnicity, etc. The computations made for determiningstructural connectivity may include, as an example, any combination of(1) generating brain structural connectivity matrix based on whitematter tractography from the whole brain; (2) generating brainstructural connectivity matrix based on white matter tractography from asubset of the brain based on another brain analysis method (such asfunctional connectivity); (3) create a matrix representing brain graymatter regions of interest based on a desired parcellation; (4)determining a value in an element of the matrix as the strength of thewhite matter connection between two corresponding regions of interest;and/or (5) using voxel-specific directional diffusion information fromdiffusion-weighted MM to produce three dimensional trajectories throughwhite matter (streamlines).

The system may be configured to select threshold percentage of brainregions that satisfy the following conditions from the abovecomputations made for determining functional connectivity of the patientbased on intra-patient comparisons:

-   -   7. Select a first threshold of regions in which the lowest        change in amplitude, frequency, frequency relative to amplitude,        or a combination thereof occurs with the largest number of brain        networks and regions.    -   8. Select a second threshold of regions in which the highest        change in amplitude, frequency, frequency relative to amplitude,        or a combination thereof occurs with the largest number of brain        networks or regions of the patient.

The comparisons are intended to identify regions of the patient's brainthat are outliers in activity, either having the lowest change or thehighest change with the most other regions and networks of the patient'sbrain. In other words, each of the computations 1-4 above can be rankedfor each region of the brain based on the change in amplitude,frequency, and frequency relative to amplitude. The regions that occurbelow a third threshold may be grouped together and the total number ofeach region within below the third threshold may be determined (thefrequency distribution of the regions below the third threshold).Similarly, the frequency distribution of the regions above a fourththreshold may be determined. The regions having the highest occurrencein each group (occurring below the third threshold or above the fourththreshold) are then used in the determination of steps 7 and 8, above.The comparisons and identified regions based on intra-patient dataprovide an intra-patient output in which a number of regions areidentified based on the analysis of the activation, correlation, andcovariation information of the patient's data.

A threshold may be used to separate the number of identified regions.The identified networks may be determined based on different thresholds.For example, the first and second thresholds to determine the finalnumber of regions may be for example, the top 1%, 5%, 10%, 15%, 25%, orother threshold. The thresholds may be the same or different. Differentthresholds may be used for different comparisons. For example, whendetermining the highest and lowest changes in amplitude, when separatingregions for the frequency distribution, the regions may be separated atthe 50% range so the top half regions are analyzed for determining thehighest change, while the lower half of regions are analyzed fordetermining the lowest change.

The system may be configured to select threshold percentage of brainregions that satisfy the following conditions from the abovecomputations made for determining functional connectivity of the patientas compared to one or more healthy control groups, inter-patientcomparisons:

-   -   9. Compare the intra-subject outputs (e.g., steps 7 and 8) with        outputs in the healthy control group having the same first        metric relationship (the same age and sex in the example using        steps 5-6), and select the regions that are outside of a        determined normal range when compared to the healthy control        group.    -   10. Compare intra-subject outputs (e.g., steps 7 and 8 or 9)        with outputs in the control group having the same second metric        relationship (the same diagnosis and symptoms in the example        using steps 5-6), and select the regions that have the maximum        change in the activation, correlation, and covariation pre- and        post-TMS treatment.

In order to save computation, the order of steps may be run in differentorders, and on different inputs. For example, the last step above, 10,may be based on the regions identified from steps 7 and 8 or those thatare narrowed by the filter from step 9.

At step 9, the system may compare the intra-subject outputs from steps 7and 8 to obtain the regions with values below a fifth threshold andabove a sixth threshold of the mean in the healthy control groupmatching the first metric relationship. The fifth and sixths thresholdmay be the same or different. The fifth and sixth threshold may be basedon a number of standard deviations, such as 1, 2, 2.5, or 3 standarddeviations away from the mean. The identified regions from step 9 maythereafter be compared in step 10 to the pool of patients matching thesecond metric relationship. In this case, a subset of regions from step9 may be selected for those regions that have the greatest change of theactivation, correlation, and covariation between the pre- and post-TMStreatments from the pool of patients of the control group having thesecond metric relationship. In other words, the regions of the controlgroup matching the second metric relationship (e.g., same diagnosisand/or symptoms, with or without the same age, gender, and/or ethnicity)are compared by taking the absolute value different between the pre- andpost-treatment for each patient in the control group. The regions withthe maximum change, greatest absolute value, are then identified as theoutput of step 10. A seventh threshold may be used to determine thecutoff, such as the top 1 percent, 5 percent, 10 percent, top 1. 2, 3,4, or more regions.

The system may be configured to use structural connections to selecttarget locations from the potential selections based on functionalconnectivity.

Embodiments may include an output including a report for the patientand/or practitioner administering the TMS treatment. The report mayinclude one or more images of the patient's brain. For example, and ofthe BOLD images, comparisons of the activation, correlation,covariation, or other combination of images used herein may be provided.The report may include a visual display of the brain map. The report mayinclude a statistical display. The report may include the recommendedregions, networks, and/or circuits for stimulation by the TMS treatment.The report may include the TMS protocol customed as needed based on thecomparisons made herein.

The stimulation parameters may also be provided that may beindividualized to the patient. Once the regions from the above steps areidentified to administer TMS, an excitatory (>5 Hz) or inhibitory (<5Hz) TMS protocol may be chosen. Treatment coils may also be chosen basedon focality and depth. Focality may be the width of the stimulated area(horizontal axes) and the depth may be the distance from the scalp totargeted area (vertical axes). Additional or alternate TMS treatmentparameters may be determined from the parameters of the prior patientgroup having the best results pre- to post-TMS treatment. The treatmentparameters from the patients that resulted in the greatest pre- andpost-changes in the brain regions from the comparison of step 10 may beused to inform the treatment parameters for administering the patient.For example, the treatment parameters may include an average, a weightedaverage, an average after filtering, or the treatment parameters fromthe patient or patients having the greatest change for that identifiedregion. The system may also compare the healthy control group data todetermine treatment parameters. For example, once the region(s) areidentified, a control group may be used to identify the patients withthe highest change pre- and post-treatment for that region based on athird relationship metric (that may or may not be the same as the firstor second relationship metric)—e.g., the control group may be used toidentify a group of patients with the highest pre- and post-changes to agiven region against the entire control group or as compared against asub-set of the control group matching the same gender, age, ethnicity,or a combination thereof (or other combination of relationship metrics).The parameters from the identified healthy control group treatment(s)may be used alone or in combination, such as an average, to create acustomized treatment protocol for the patient.

FIGS. 3A-8B illustrates flow diagrams and corresponding brain visualsfor explaining the exemplary functional connectivity analysis. Thesystem may be configured to perform any combination of the followingfunctional connectivity analysis:

-   -   1. Network to network, inter-network analysis by computing a        change in amplitude, frequency, and frequency relative to        amplitude between different brain networks of the same patient,        for each network of the patient;    -   2. Region to network, intra-network analysis by computing a        change in amplitude, frequency, and frequency relative to        amplitude between brain regions with a network and all other        members of the network of the same patient, for each region of        the patient.    -   3. Region to region, intra-network analysis by computing changes        in amplitude, frequency, and frequency relative to amplitude        between different brain regions within a network and all other        members within the network of the same patient, for each region        of the patient;    -   4. Region to region, inter-network analysis by computing changes        in amplitude, frequency and frequency relative to amplitude        between different brain regions of different networks of the        same patient, for each region of the patient.    -   5. Statistical comparison of measurements of any combination        of (1) through (4), above, from the patient compared to similar        functional connectivity analysis of a healthy control group        comprising individuals matching the same age and sex as the        patient;    -   6. Statistical comparison of measurements of any combination        of (1) through (4), above, from the patient compared to similar        functional connectivity analysis of a healthy control group        comprising individuals matching the same age, sex, and diagnosis        and/or symptoms as the patient.

Systems and methods may include determining a change in amplitude,frequency, and/or frequency relative to amplitude between differentbrain networks to networks, regions to regions, or regions to networks.The differences are determined based off of the spontaneous BOLDcontrast fluctuations when the subject is at rest (the subject ispresented with no specific stimulus or task). The BOLD contrastfluctuations may be determined from fMRI by comparing different brainregions and networks. The brain may be divided into different regions.The regions may be based on recognized brain regions, or othersubdivisions based on the activity of regions of the brain based ondifferent functional, connective, and/or developmental criteria, or asindicated in the fMRI images of the patient and/or of a healthy patientgroup. The networks of the brain may be identified as collections ofregions. The collection of regions may be based on functionalconnectivity by statistical analysis of the fMRI BOLD signal of thepatient and/or based on recognized networks of healthy patients. Otherdelineations of regions and networks may be used. FIG. 3B illustrates anexemplary brain subdivided in an exemplary 4 networks identified as n=1,n=2, n=3, and n=4. FIG. 3D illustrates the exemplary brain of FIG. 4Bsubdivided further into individual regions so that each networkcomprises a plurality of regions. The number of regions per network maybe the same or different. For example, the first network, n=1, of FIG.3B, is subdivided into four regions, while the second network, n=2, issubdivided into three regions. The illustrated regions and networks arefor provided only to explain the associated flow diagrams and are notintended to be limiting as to actual brain regions and/or networks.

The exemplary functional connectivity analysis comprises variousanalysis of the frequency, amplitude, and frequency relative toamplitude changes based on the fluctuations of the spontaneous bloodoxygenation level-dependent as determined from the fMRI. The comparisonsare based on the regions and networks of the brain. Therefore, FIG. 3Aillustrates a flow diagram for calculating the activation (amplitude ofthe spontaneous BOLD fMRI fluctuations), correlation (frequency of thespontaneous BOLD fMRI fluctuations), and covariation (correlation toactivation of the spontaneous BOLD fMRI fluctuations) for the variousbrain networks, while FIG. 4A illustrates the calculations of theactivation, correlation, and covariation for the various brain regions.

Referring to FIG. 3A, the BOLD fluctuations of the first brain networkarea is determined for each network of the brain. The network may beassociated with a given value by determining a medium, average, or otherstatistical value associated with a distribution to associate anamplitude and/or frequency of the BOLD fluctuations to the givennetwork. As illustrated, the analysis starts with the first network,n=1. If not all of the networks have been analyzed, which would not bethe case of the first network, then the system calculated the activationfor the first network and the correlation for the first network. Fromthese values, the system can then calculate the covariation as the ratioof the correlation to the activation (or vice versa). The system thenincrements to the next network to determine the activation, correlation,and covariation thereof. The system progresses through each networkuntil all networks (n≤N) have an associated activation, correlation, andcovariation value.

Referring to FIG. 4A, the BOLD fluctuations of the first brain region isdetermined for each region of the brain. The region may be associatedwith a given value by determining a medium, average, or otherstatistical value associated with a distribution to associate anamplitude and/or frequency of the BOLD fluctuations to the given area.As illustrated, the analysis starts with the first region. If not all ofthe regions have been analyzed, which would not be the case of the firstregion, then the system calculates the activation for the first networkand the correlation for the first network. From these values, the systemcan then calculate the covariation as the ratio of the correlation tothe activation (or vice versa). The system then increments to the nextregion to determine the activation, correlation, and covariationthereof. The system progresses through each region until all nodes(r≤R_(max)) have an associated activation, correlation, and covariationvalue.

As used herein, all of the activation, correlation, and/or covariationof each network and/or region may be calculated and/or saved to adatabase. As described herein with respect to FIGS. 3A-8B, thecovariation may be saved and analyzed between the various regions andnetworks. Therefore, the activation and correlation may be calculatedduring the calculation of the covariation, but not separated determinedand/or saved to the system. Such intermediate determination isunderstood to be included in the definition of calculation theactivation and/or correlation as used herein.

As illustrated, each network is identified is a numerical sequentialvalue, while each region is identified as its network, and sequentialnumerical value for the region. Systems and methods may use any indexingsystem to associate the activation, correlation, and/or covariation ofthe given network and/or region. Therefore, as explained in theassociated flow diagram of FIG. 4A, when comparing r≤R_(max), the givenvalue of R is not necessarily restricted. Instead, the comparison isintended to illustrate that the current region is one of the regionsthat needs to be analyzed and has not previously been analyzed.

Exemplary embodiments include comparing each of the brain networks toeach of the other brain networks. As illustrated in FIG. 5A, a firstnetwork selected to compare to all other networks. For example,n_(a)=(n=1) from FIG. 3B, as indicated by the cross hatching of thisnetwork in FIG. 5B. That first network is then compared to the nextnetwork (n=2) by taking the difference of the covariation values fromthe information as determined from the algorithm described with respectto FIGS. 3A and 3B. That same first network is then compared to the nextnetwork (n=3) by taking the difference of the respective covariationvalues. Once the first network has been compared to all other networks,then the next network (n=2), is then compared against all othernetworks. The difference between a previous comparison may not berepeated. Therefore, if the difference of n1 from n2 has already beendetermined, then the difference of n2 from n1 is not necessarily alsodetermined. Therefore, as next network is compared against all otherhigher order or previously uncompared networks. The comparison looptherefore is illustrated in terms of n_(b)=n_(a)+1 so that allpreviously uncompared networks from n_(a) may be compared against n_(a).The loop continues until all of the networks have been compared againstall other networks. As illustrated in FIG. 5B, only the inner loop isillustrated in which n_(a)=1 has been compared to all other networks(n_(b)=n_(a)+1=2 to n_(b)=N).

Embodiments include comparing each of the brain region to each of theother brain regions. The comparison may be handled in two segments inwhich a region within a network is compared against all other regionswithin the same network, region to region, intra-network as illustratedin FIGS. 7A to 7B, as well as each region within a network is comparedagainst all other regions within the other networks, region to region,inter-network as illustrated in FIGS. 8A to 8B. The combination of thesetwo loops results in each region being compared against each otherregion of the brain by taking the difference of the respectivecovariation values between each region. In other words, a first regionselected to compare to all other regions, such as r_(a)=(r_(n=1)=1) fromFIG. 4B, as indicated by the cross hatching of this region of FIGS. 7Band 8B. Referring to FIG. 8B, that first region is then compared to thenext region within the network (r_(b)=(r_(n=1)=2)) by taking thedifference of the covariation values from the information as determinedfrom the algorithm described with respect to FIGS. 4A and 4B. Referringto FIG. 8B, that first region is also compared against the next regionwith the next network (r_(b)=(r_(n=2)=1)) by taking the difference ofthe covariation values from the information as determined from thealgorithm described with respect to FIGS. 4A and 4B. As the algorithmloops through either FIG. 7A or 8A, that first region is comparedagainst the next region within the first network (FIG. 7B) or the nextnetwork (FIG. 8B). The respective loops continue until each region hasbeen compared against each other region by determining a difference inthe covariation between each of the regions.

Embodiments include comparing each of the brain regions to each of thebrain networks that does not include the brain region. As illustrated inFIG. 6A, a first region is selected to compare to all other networks.For example, r=(r_(n=1)=1) from FIG. 4B, as indicated by the crosshatching of this region in FIG. 6B. That first region is then comparedto the next network (n=2) by taking the difference of the covariationvalues from the information as determined from the algorithm describedwith respect to FIGS. 3A-4B. That same first region is then compared tothe next network (n=3) by taking the difference of the respectivecovariation values. Once the first region has been compared to all othernetworks, then the next region (r=(r_(n=1)=2)), is then compared againstall other networks. The loop continues until all of the regions havebeen compared against all of the networks that does not include theregion being compared. The loop continued until all of the regions havebeen compared against all of the networks, regardless of whether aregion is included in a network or not. As illustrated in FIG. 6B, onlythe inner loop is illustrated in which r=(r_(n=1)=1) has been comparedto all other networks (n_(b)=n_(a)+1=2 to n_(b)=N).

After all of the comparisons above are determined, (network to network,region to region, and region to network), a statistical distribution maybe created to obtain a first subset of regions and/or networks in whichthe differences from the above results (difference of covariation) isthe lowest for the largest number of brain networks/regions and a secondsubset of regions and/or networks in which the differences from theabove results (difference of covariation) is the highest for the largestnumber of brain networks/regions.

The first and second subset of regions and/or network are then comparedagainst a healthy control group with the same age and/or gender as thepatient. A third subset of regions and/or networks are identified fromthe first and second subsets where the regions that fall outside of athreshold from the mean of values of the covariation differences fromthe healthy group. For example, those regions that have correspondingcovariation differences that are outside of (more or less than) twostandard deviations from the mean of covariation differences fromhealthy individuals matching the same gender and age. The firstthreshold value may be within two standard deviations above a mean ofthe healthy control group, and the second threshold value may be withintwo standard deviations below the mean of the healthy control group.Matching a healthy control group may be against the patient based on adifferent range of parameters as would be understood by a person ofskill in the art. For example, the comparison may be an exact match sothat the gender and age of the patient is the same as those from thecontrol group. The same age may be based on birth year. The comparisonmay also be on a range as would be understood by a person of skill inthe art. For example, medical age groups are used in medicalcomparisons. Therefore, the same age may be the same age within the sameage grouping. Exemplary age groupings may be 18-21, 22-35, 36-55, 56-65,66 and older. Other groupings may also be used, such as for example,18-24, 25-45, 46-65, and over 65. Other comparison ranges may also beused. For example, the same age based on birth year plus or minus adeviation, such as a year or five years. The matching may be based onranges based on the condition and the changes a patient typicallyexperiences based on the age range.

The first and second subset of regions and/or network also oralternatively may be compared against a healthy control group with thediagnosis/symptoms pre/post TMS treatment. A fourth subset of regionsand/or networks are identified from the first and second subsets or fromthe third subset of regions and/or networks for those networks that areindicated as having the largest changes in covariation values from pre-to post-TMS treatment for the pool of patients with the same diagnosisand/or symptoms. In other words, an exemplary input to the systemcomprises a comparison pool of patient data in which the covariationdifference before and after TMS is taken for each region and/or networkof the patient's brain. The comparison pool also comprises the diagnosisand/or symptoms for the patient. Each of the regions and/or networksfrom the third subset of regions is then compared to comparison pool todetermine an associated difference of covariation of the respect regionand/or network for the patients that have undergone treatment. Thefurther subset therefore identifies those regions that are most likelyto provide results for TMS treatment by having significant changesbecause of the TMS.

Embodiments may include comparisons of different features of an fMRIfrom different regions, networks, etc. of the brain. Relativecomparisons may be used in which high, low, large, small, etc. arewithin the scope of the methods, systems, algorithms used herein. Aperson of skill in the art will understand the scope of these relativeterms by referencing the purpose of the comparison and the relativevalues in the value set in which the relative term is used. For example,“high” compared to a data set may be 50% of the values in the higher endof the available range of data, or may be 75%, 80%, 90%, 95%, or othermargin. A person of skill in the art will appreciate that the selectionof the range may be informed by the purpose described in which thecomparison is made. The relative term may be equal to, above, or below aset number (a threshold) or range as would be understood by a person ofskill in the art based on the characteristic of the value, the purposeof the comparison, the function of the treatment or algorithm, thenormal or average of a healthy control group, a range or one or morestandard deviations away from normal or average of a data set for thecharacteristic (whether of a control group, healthy group, illness,comparison to other data within a data set, etc.). The setting of athreshold may be based on a statistical variation and/or in selecting adesired number of target locations for treatment.

An embodiment comprises a first input comparison control groupcomprising patient information including the patient's age, sex, gender,and/or other relevant diagnosis and/or treatment parameter. The firstinput comparison control group comprises a database of data from healthypatients comprising comparisons (differences) of covariation betweenregion to region, region to network, and network to network. The firstinput comparison control group may then be used to compare theintra-subject outputs in order to determine those regions from thepatient that are out of a desired range from those as compared to ahealthy control group as represented by the first input comparisoncontrol group. The healthy control group may be compared on the samedifferences and regions with the largest difference from normalcy (thehealthy group) are chosen as targets. In other words, if the patientgenerated a high covariation difference for region 1 from network 1because of a comparison from region 1 from network 2, the healthypatients may be compared on that same difference (region 1, network 1 toregion 1, network 2). Embodiments may also make comparisons based onindividuals from the control group, averages over the control group(e.g., the healthy control group is averaged over all of the same regionto network and region to region differences), or other basis ofcomparison.

An embodiment comprises a second input comparison control groupcomprising patient information including the patient's age, sex, gender,and/or other relevant diagnosis and/or treatment parameter. The secondinput comparison control group comprises a database of data frompatients having the same symptoms and/or diagnosis where the dataincludes a covariation of each treatment region of the patient's braincompared before and after TMS treatment. In other words, the secondinput comparison control group comprises information about the changesthat occur in regions of a patient's brain based on the application ofTMS treatment to that region and the covariation is determined beforeand after the treatment to that region.

Embodiments may include comparing regions and networks of a patient'sbrain to other regions and networks. The comparisons may be performed indifferent ways. For example, as shown and described in the loops ofFIGS. 3A-8B, each region may be compared against each other region, eachnetwork compared to each other network, each region compared to eachnetwork (without or without the network in which the region itselfresides), or any combination thereof. In comparing a region to otherregions, or to other networks, or networks to other networks, acombination of the other regions or networks may be in groups. Forexample, each region may be compared against each of the other regionsindividually, but also as a group. The comparison by a group may be bytaking an average of the regions over the group of regions so that thecomparison is being performed by a single region compared against agroup of regions. A region is compared against a group of the otherregions that occur within the same network. A region is compared againsta group of the regions that comprise a network or a portion of anetwork, where the group of regions and the network are outside of theregion or network in which the single region is located. The region iscompared against a plurality of groups of regions so that the region iscompared against all other regions of the brain.

The comparison of regions to regions, regions to networks, and networksto networks may not include all other regions and/or networks in abrain. The comparison of regions to regions comprise comparing regionsto regions within the same network and/or regions outside of the samenetwork. However, if portions of the brain are known to be functioningproperly or not related to a given ailment or symptoms, then region(s)or network(s) may be removed from the comparison or regions andnetworks. However, it is still preferrable to compare regions to otherregions outside of the same network, networks to networks, or regions tonetworks outside of the network of the region for a more complete brainassessment for determining targeting of TMS treatments.

Embodiments may be used to treat various conditions. For example,embodiments may be used to treat various mental disorders, such asAlzheimer's disease, anxiety, obsessive compulsive disorder (OCD), PTSD,schizophrenia, cognitive impairment caused by stroke and other brainlesions, cognitive impairment caused by traumatic brain injury (TBI),insomnia, eating disorders, drug addiction, depression, attentiondeficit hyperactivity disorder (ADHD), attention deficit disorder (ADD),bipolar disorder, autism spectrum disorder, neurodevelopmental disordersand psychoses. Exemplary embodiments may also be used to reducecognitive decline.

Embodiments of the present disclosure may be used with patientssuffering from psychoses (auditory hallucinations). For example, FIGS.9A-9F illustrates pre-TMS treatment, and FIGS. 10A-10F illustratespost-TMS treatment averaged across five patients suffering from AuditoryHallucinations (AH) psychoses in which the target brain regionsidentified for TMS treatment are outlined in bold lines. The illustratedimages are from the covariation (amplitude to frequency) comparison ofthe BOLD fMRI data. The identified circuits for treatment according tothe exemplary regions for treatment included in the Left Hemisphere:PGs, IP1, MIP, 7PL, VIP, IPS1, 10v 10r, 25, and the Right Hemisphere:7PL, MIP, IP1, VIP, 7PC, OFC, pOFC, 25. As illustrated in FIG. 10A-10F,the circuits after TMS treatment are normalized. The patientsexperienced changes in their PANSS scores from severe (score of 6) tominimal (score of 2) in areas including P1Delusions, P2Conceptualdisorganization, P3Hallucinatory behavior, P6Suspiciousness/presecutionand P7Hostility. Patients were also able to reduce their medications to50 percent after the time of their post-fMRI scan.

The scans of FIGS. 9A-10F are averaged over five patients suffering fromAuditory Hallucinations (AH) psychoses so as not to disclose personalscans. As illustrated, the lighter areas of FIGS. 9A-9F were targetedfor stimulation treatment and find improvement as illustrated in theimproved areas illustrated in FIGS. 10A-10F.

Embodiments of the present disclosure may be used for patients sufferingfrom TBI. For example, FIGS. 11A-11B illustrates an exemplary baselineRSFC area map pre-TMS treatment according to embodiments, with FIGS.12A-12B illustrating an exemplary RSFC map after the fMRI guidedtreatment. FIGS. 13A-13B illustrates an exemplary control group havingthe same age and sex as the patient from FIGS. 11A-12B. Theillustrations are averaged over 5 exemplary subjects for the sake ofillustration. The RSFC analysis according to exemplary methods describedherein was run on five patients. The exemplary embodiments may identifycircuits for treatment with TMS including the following regions withinthe language network for stimulation: Left Hemisphere: STSdp, TPOJ1,STV, SFL, 55b, 44, 45. Exemplary embodiments of the treatment protocolwere excitatory, at 130% amplitude of individual motor threshold. Thepatients experienced symptoms including limitations of languageproduction (speech comprehension, articulation and speed) and languagecomprehension (semantic processing) from severe (score 6) to mild (score3). Exemplary maps provided for different RSFC areas are provided forexample. See FIGS. 11A-31B.

A sudden blow or jolt to the head could cause TBI. Functional damage ofthese injuries has been investigated specifically in intrinsicconnectivity networks (ICNs). TBI substantially disrupts ICN functionassociated with cognitive impairment. The two primary ICNs, the saliencenetwork (SN) and the default mode network (DMN) are believed relevant totreating TBI. In addition to the above findings, it is believed that theefficacy of standard TMS in treating TBI could be substantially improvedby arming it with a guiding system based on brain networks.

Resting-state functional connectivity MM (rsFC Mill) is an imagingtechnique capable of revealing the brain networks and the dynamics oftheir interaction. Embodiments may use rsFC MRI to visualize eachpatient's brain networks. RsFC MRI maps may also be used to findappropriate targets that will “normalize” the brain network's functionand interaction with other networks. This is an effective way oftreating TBI disabilities since, e.g., activity of ICNs is normallytightly coupled, which is important for attention control. The abilityto restore the normal activity of ICN could reverse the loss of tightcoupling of ICN as a result of damage to the structural connectivity ofthese networks. Loss of normal correlation between networks may produceabnormalities of network function and cognitive control.

Conventionally, the patient or those near the patient are required toprovide symptoms in order to determine the severity of TBI. In someinstances, physical detection may be made through structure lesions inthe brain. The brain suffering neurological death will show lesions inthe form of volume loss of the brain tissue at the structurally damagedareas of TBI. Essentially, these areas have no connectivity to otherareas of the brain. Structural lesions can be detected through methods,systems, and instructions described herein through the comparison ofrsFC MM comparisons by identifying areas that have no functionalconnectivity with a large number of brain areas to suggest a globaleffect. Other methods descried herein may use structural analysis toidentify total cortical gray matter volume and white surface total areafor identifying structural changes associated with TBI. Embodiments maybe used to provide alternative diagnosis pathways for identifyingstructure lesions decoupled from the physical detection through braintissue loss.

The systems, methods, and instructions provided herein may also be usedto diagnose brain injuries that will lead to structural lesions beforeneurological death so that the brain areas may be treated or in whichinjury may be reduced. For example, The systems, methods, andinstructions provided herein may identify functional lesions. Afunctional lesion may be an area of the brain that is functionallydisconnected from a large number of brain areas; but may not yet be zeroor may not have undergone physical loss. Areas with functional lesionsmay show an erratic or paroxysmic behavior, having a high and negativecorrelation/covariation less than zero. For example, one region ascompared to other regions of the brain using rsFC MRI may show high andnegative correlations so that one area is identifying as having veryhighly negative correlation to other areas of the brain. In thisinstance, the brain area may be considered as a functional lesion that,if left untreated, will have a high potential or likelihood to result instructural lesions and/or neurological death of the brain area.

In functional lesions, a global disruptive behavior by deactivatingother brain regions with which they are negatively correlated may bedetected. Functional lesions regions are highly negatively correlated;meaning that in an attempt to reconnect itself with the rest of thebrain, the region deactivates a large number of brain regions, shuttingdown regions to which it is negatively correlated. High and negativecorrelation/covariation with a large number of brain regions may be usedto identify functional lesions; these high and negativecorrelations/covariations are less than zero and show a large-scaledecoupling effect (deactivation of a large group of brain regions).

The systems, methods, and instructions may therefore include early stagediagnosis and treatment options for detecting areas in jeopardy of braindamage before structure detection is evident or available. Using thefMRI to compare functional connections of brain areas, the systems,methods, and instructions may provide early detection of future braininjury by identifying areas of the brain that are functionallydisconnected, but which have not yet experienced neurological deathand/or physical deterioration. Embodiments may therefore provide earlydetection in order for treatments to intercede before neurologicaldeath, to limit damage, and/or to reverse damage.

Embodiments may therefore be used to diagnose and treat TBI. Embodimentsmay not need to rely on the patient's physical symptoms or physicalbrain lesions to diagnose the severity of TBI. Embodiments may be usedfor early detection and/or treatment before brain damage or beforesignificant brain damage or before neurological death. The structurallesion may have zero functional connectivity, or nearly zeroconnectivity with many brain regions, including, reduced, or near zerofunctional connectivity. FIGS. 19A-20B illustrates brain scans for apatient having experienced a stroke. FIGS. 19A and 20A illustrate imagesfrom an fMRI brain scan for patients experiencing a stroke showing closeto zero functional connectivity. FIGS. 19B and 20B illustrate thecorresponding image to show the corresponding structural degradation.The patient scans show no correlation/covariation with a large group ofbrain regions. The exemplary embodiment shows correlation/covariationvalues of 0 or close to 0.

In stage 1, prior to neuronal death, correlation/covariation may be lessthan zero with a large number of brain regions, indicating globaldecoupling effect, deactivating a large number of brain regions andcategorized as functional lesions. In stage 2, when brain damage isusually present, correlation/covariation is near or at zero with a largenumber of brain regions, showing a pattern of global non-connectivityfor structural lesions. The change in connectivity of an area toidentify TBI may be according to the embodiments described herein inwhich an area of the brain as compared to all other areas of the brainstands outside a statistical deviation from the rest as having a lowfunctional connection as a structural lesion or a high and negativecorrelation as a functional lesion. The comparison may also oralternatively be based on other patients that have been diagnosed andtreated for TBI.

Embodiments of the methods may use advanced techniques to model braindynamics to get insight into network dysfunction. For example,embodiments may use rsFC MRI to find out how structural network damagecaused by axonal injury produces functional irregularities, which may beused to determine targets for TMS treatment. As another example,embodiments may detect “functional lesion(s)” to detect and/or treatbrain areas in certain patients with TBI that are not detectedstructurally. Embodiments may be used to identify “lesions” when thebrain area has not materialized structurally in the form of volume loss(neuronal death). By early treatment provided by early detection,structural damage may be limited, stopped, or even reversed by treatingthese early diagnosed lesions.

Embodiments herein may be used to identify structure lesions by findingareas of the brain without functional connection to other areas of thebrain. In this instance, these areas of the brain may be targeted and/oreliminated from treatment. For example, these areas may be removed fromthe algorithms herein as the brain area may have already experienceneurological death and therefore would not be receptive to treatment.Without removing these areas from selection, they may be identifiedthrough the above algorithms based on the criteria above as theirdysconnectivity may identify these areas as preferred treatment areas.In other instances, if neurological death has not yet occurred, theseareas may be highly desirable to treat before such physical damage iscompleted or continued. The pattern of connectivity of structural lesionversus functional lesion is very different.

Embodiments of the fMRI-guided TMS treatments herein may be used totreat functional lesions that may result from TBI. Brain maps offunctional lesions are usually very abnormal (erratic or paroxysmicbehavior of neurons), usually with high and negativecorrelation/covariations with a large number of brain regions showing aglobal effect. Usually, these maps are very colorful in the negative endof the spectrum, i.e., light blue, green, purple, grey, etc. Theselesions can fully recover or improve their function when these areas aretargets with TMS treatments. FIGS. 18A-18B illustrates a frontal polarcortex (area TE1m, right) having a high negative correlation/covariationwith a large group of brain regions (light blue and green areas). FIG.18A illustrates a patient at baseline; and FIG. 18B illustrates apatient after treatment according to embodiments described herein.

Embodiments of MRI-guided TMS treatments herein may be used to treatstructural lesions that may result from TBI. Brain maps of structurallesions are usually non-colorful, i.e., dark red or brown, almost black,showing lack of connectivity with a large number of regions. In someinstances, some activity may still be detected in these areas. If so,then these areas may partially recover their function after treatmentwith TMS.

Patients with TBI might show either or both type of function and/orstructural lesions. Embodiments herein may be used to treat either orboth with fMRI-guided TMS. Embodiments of the present disclosure may beused for patients to reduce further damage from TBI or reduce braindamage from functional disconnectivity. Embodiments herein may be usedto provide early-stage diagnosis and treatment of brain areas prior tostructural brain damage by identifying and/or treating functionallesions.

Embodiments of the present disclosure using fMRI may allow earlydetection of brain abnormalities prior to behavioral or structuralmanifestations (i.e., imaging-based biomarkers) for prevention and earlyinterventions in neurological and psychiatric conditions. Embodimentsmay also allow interventions in later stages of the disease/conditionwhen cortical atrophy and volume loss are present by: (1) recruitinghealthy brain regions in affected pathways to compensate for functionloss and; (2) stimulating atrophic areas to delay further deteriorationand progression of the disease (when applied to neurodegenerativediseases like in Parkinson or Alzheimer's disease, where conditions getworse over time).

Embodiments may show and describe methods of identifying brain regionshaving high negative correlation and covariation with a large group ofbrain regions. These types of functional lesions can be detected in anybrain region. The embodiments illustrated herein show single casescenarios for illustration. See FIGS. 99A to 127 . Embodiments may beused to help prevent late complications of TBI, such as Alzheimer'sdisease and chronic traumatic encephalopathy. This may be due to thefact that axonal injuries could interact with neuroinflammation andneurodegeneration contributing to the formation of chroniccomplications. Embodiments may use network-level imaging to informdiagnosis, prognosis, and treatment planning for TBI.

Embodiments of the present disclosure may be used for patients to reduceor prevent cognitive decline based on aging. For example, FIGS. 32A-32Dillustrate a baseline RSFC area map pre-TMS treatment, with FIGS.33A-33D illustrating an RSFC map after the fMRI guided treatment. FIGS.34A-34D illustrate a control group having the same age and sex as thepatient from FIGS. 32A to 33D. The illustrations are averaged over five(5) subjects for the sake of illustration. The RSFC analysis accordingto methods herein was run on five patients. The embodiments hereinidentified circuits for treatment with TMS including the followingregions within the frontoparietal and language networks for stimulation:Left Hemisphere: p9-46v, STSdp, TPOJ1. Embodiments of the treatmentprotocol were excitatory, at 130% amplitude of individual motorthreshold. Patients were asymptomatic at the time of TMS treatment, sono change in symptomatic score was produced. The patients were selectedfor their risk factors for neurodegenerative diseases (cardiovascularand genetic predisposition). Exemplary maps provided for different RSFCareas are provided for example. See FIGS. 32A to 38D.

Aging can cause cognitive impairment, such as impairments caused byvascular degradations. Vascular cognitive impairment (VCI) is caused byblockage of micro vasculatures; and the inability to supply oxygen andnutrients to different brain regions causes a decline in cognition.Sometimes, this inability is caused by cerebrovascular injury. Inaddition to vascular-based dementia there is also degenerative dementia(which is most common in Alzheimer's disease). Combining these twotypes, covers the majority of cases of dementia.

TMS can be used to prevent cognitive decline by strengthening brainactivity and connectivity in most vulnerable regions, like specificlocations within the frontal and temporal lobes detected by ourfMRI-based biomarkers. The methods herein may be successful in treatingMCI patients using MRI guided TMS approach. Embodiments may also be usedto develop a new protocol for preventing or reducing the decline ofeveryday memory in asymptomatic patients with risk of MCI andAlzheimer's disease, due to family history or cardiovascular disease.

Embodiments of a protocol may involve: (1) the analysis of resting statefMRI (rsfMRI) images taken of the patient's brain to construct brainnetworks, (2) the use of biomarkers to detect most vulnerable brainregions in each individual, and (3) the delivery of MRI guided TMS overa course of 20 sessions. Patients may receive a second MRI after thelast session to compare baseline and post-treatment brain maps tocalculate the efficacy measure. Patients may also receive another MRI atone-year follow-up to monitor evolution of brain activity andconnectivity, and the need of another cycle of 20 sessions with MMguided TMS may be assessed by biomarkers, and delivered as needed overthe course of the lifespan. The methods herein for use with MCI patientssuggest that MRI guided TMS could be an effective therapy for preventingcognitive decline and probably a tool to delay deterioration.

Embodiments of the present disclosure may be used for patients sufferingfrom depression. For example, FIGS. 41A-41D illustrate a baseline RSFCarea map pre-TMS treatment according to embodiments herein, with FIGS.42A-42D illustrating an RSFC area map after the entire circuit basedfMRI guided treatment according to embodiments herein. FIGS. 43A-43Dillustrate an RSFC area map after a single region treatment of area 46within the DLPFC according to conventional treatments. FIGS. 44A-44Dillustrates an RSFC area map from the healthy control group matching thesame age and sex as the patient from FIGS. 41A to 42D. The illustrationsare averaged over ten (10) subjects for the sake of illustration. TheRSFC analysis according to methods herein was run on ten patients withdepression who did not respond to standard TMS approaches based on the5-cm rule. The embodiments herein identified circuit including thefollowing regions within the cingulo-opercular network for stimulation:Left Hemisphere: 46, PF; Right Hemisphere: 46, PF. Embodiments of thetreatment protocol were excitatory, at 120% amplitude of individualmotor threshold. Change symptomatic depression scores based on HAMDincluded starting from a baseline score of HAMD<7 in all subjects,remission after circuit-based fMRI-TMS, and HAMD>7 in all subjects afterthe single-region fMRI-TMS, where 50% improvement was achieved in HAMDscores in all subjects but none achieved remission. None of the patientsincluded in these analyses responded to standard-TMS (5-cm rule).Exemplary maps provided for different RSFC areas are provided forexample. See FIGS. 41A-7A-1 to 56D.

Major depression disorder (MDD) may be characterized by abnormalfunctional connectivity of brain networks. Research has revealeddisrupted network connectivity in main MDD networks, i.e., default modenetwork (DMN), the central executive network (CEN), and the saliencenetwork (SN). There have also been reports of abnormality in cerebellarand thalamic circuits. The methods may deal with the qualitative aspectsof the symptomatology of MDD using a unique tool (resting-statefunctional connectivity MRI or fMRI) that is capable of revealingalterations in the brain networks.

Embodiments herein may be used to identify the severity of depressionwith the alterations found in the DMN, CEN, SN, frontal and thalamicbrain regions, insula, and the subgenual anterior cingulate cortex(sgACC). One reason that standard TMS does not produce very highoutcomes is due to its inability to treat targets unique to eachpatient. Embodiments described herein may measure the patient's brainfunction and connectivity shortly before treatment begins, and using asophisticated algorithm, identify specific brain regions that showabnormal behavior.

Embodiments may be used to show a clear functional connectivityinvolvement in MDD that is associated with its pathophysiology andtreatment. However, embodiments of the use of fMRI enables embodimentsof the method described herein to find targets, deliver treatments andassess their efficacy upon completion of treatment. Implementations offunctional connectivity as a scientific biomarker may increase thechances of addressing the root cause of MDD. Just like any otherdisorder, diagnosis of the brain regions involved in depression in orderto target them is important. Using fMRI, the methods described hereinmay find the brain regions which are not functioning normally. ExemplaryfMRI-guided TMS protocols of depression of embodiments described hereinmay identify and treat these specific regions, most famously called theDLPFC. We have found decreased activity and abnormal connectivity in theleft DLPFC of depressed patients; and this area is associated withbehavioral dysregulation common in depression (e.g., decreased energy,insomnia, appetite changes).

The involvement of subgenual anterior cingulate cortex (sgACC) in thepathophysiology of depression and as a predictor of response is alsoshown. DLPFC hypometabolism might be secondary to limbic hyperactivity,and that DLPFC connectivity (negative correlation) with limbic regionsare responsible for antidepressant response. It may be that optimaltargets for TMS fall broadly within the sgACC “anticorrelated” region ofDLPFC2-4. When a patient is depressed, there are certain areas in thebrain that may be affected, for example, the dorsolateral prefrontalcortex, the hippocampus, amygdala, the lateral orbitofrontal cortex, theanterior cingulate cortex, and the posterior, the parahippocampal gyms,the insula, the temporal cortex, and the precuneus are all regionsvisualized and treated according to embodiments of regions and networksanalyzed according to the method described herein for depressiontreatment. When blood flow and connectivity in these areas is outsidethe norm, further investigation may confirm that there is an area thatis over- or under-functional and/or structural and that the functionaland/or structural connectivity may be increased or decreased. By settingthe TMS coil to deliver magnetic pulses in these specific areas at aspecific frequency to target the circuitry there, then the specificregions of the brain integral to the symptoms can be brought back intoproper function and the patient can feel joy again.

Systems and methods described herein may be used for patients sufferingfrom autism spectrum disorders. For example, FIGS. 57A-57D illustrate abaseline RSFC area map pre-TMS treatment, with FIGS. 58A-58D illustratesan RSFC map after the fMRI guided treatment. FIGS. 59A-59D illustrate anexemplary control group having the same age and sex as the patient fromFIGS. 57A-58D. The illustrations are averaged over 5 exemplary subjectsfor the sake of illustration. The RSFC analysis according to methods wasrun on five patients. The embodiments identified circuit for treatmentincluding the following regions within the default and language networksfor stimulation: Left Hemisphere: STGa, STSda, STSva, STSdp, STSvp.Embodiments of the treatment protocol were excitatory, at 110% amplitudeof individual motor threshold. The patients experienced symptomsincluding limitations of social interaction, social communication,theory of mind starting at severe (score 6) and improved to mild (score3) in all subjects. Exemplary maps provided for different RSFC areas areprovided for example. See FIGS. 57A-71D.

Autism in DSMS is characterized as one of the so-called pervasivedevelopmental disorders (PDD) which also includes Asperger's disorderand other pervasive developmental disorders not hereto specified. Thesegroups of disorders are collectively called autism spectrum disorders(ASD). ASDs are characterized by impairments in social skills,repetitive behavior, and the communicative use of verbal and nonverballanguage. Children with restricted and repetitive behavior sharefeatures such as intricacies of behavior and the inability to graspconcepts. ASD affects about 1 in 60 children, but there are currently nopharmaceutical treatments that can target the core networks implicatedin ASD.

Recent evidence shows that repetitive transcranial magnetic stimulation(rTMS) holds high potential to alleviate the main symptoms ofindividuals with ASD. Individuals with autism have a Unique BrainComplexity. There is strong evidence that rTMS improves symptoms inyoung people with ASD. Our experience indicates that rTMS applied toDLPFC that is targeted to individual regions specific to the individualbased on the embodiments described herein has efficacy as a novelintervention for EF deficits in ASD. By leveraging the methods of rsFCMM technology described herein, TMS can be administered in a targetedand individual way to the patient with the capability to directly treatmalfunctioning regions and acquire pre-/post-treatment neuroimagingmeasures, to document the effect of treatment on brain structures thatare essential for EF performance. Embodiments of this capability allowsus to examine the neural mechanisms for rTMS treatment efficacy.Embodiments may also include a longitudinal follow-up that enablesassessment of the need for ongoing intervention to maintain treatmenteffects.

As in depression, the standard rTMS in ASD uses the site of treatmentbased upon fixed location relative to the motor cortex. Since autism isa disorder of the association cortex, and in particular, a disorder ofconnectivity that primarily involves intra-hemispheric connectivity, fortreating ASD a connectivity-based targeting strategy for TMS would be toidentify optimal TMS target coordinates in the bilateral DLPFC. Ourprior experience suggests that in ASD with a predominance ofsocial-cognitive malfunctions, embodiments of the method provided hereinenables: (i) measurement changes in resting-state functionalconnectivity (rsFC) between nodes of involved networks, e.g., Theory ofMind (ToM) network of prefrontal cortex, orbitofrontal cortex,supplementary areas, anterior cingulate cortex, posterior cingulate,superior temporal cortex, superior/middle temporal gyms and inferiorparietal lobe regions, with the culprit deep nuclei regions, e.g.,ventral anterior nucleus of the thalamus. Such comparisons yieldcorrelations that mediate treatment response; and (ii) aconnectivity-based targeting approach applied at the single-subjectlevel to identify optimized bilateral targets in the prefrontal cortexfor TMS to individualize therapy. We, therefore, offer a novel,innovative approach with enhanced efficacy TMS guided by rsFC MM.

This approach is fundamentally different from traditional approachesthat target without looking into a patient's brain or understandingdifferences in brain organization. Instead, embodiments described hereinstimulate the brain based on the individual brain's rsFC pattern.Embodiments described herein emphasize the network architecture of thehuman brain and as such use rsFC MRI to construct the networksimplicated in each disorder. Concerning ASD, evidence such as thegeneralized dysfunction of the association cortex with sparing ofprimary sensory and motor cortex and white matter, combined with theabsence of clinical signs of focal brain dysfunction, common in childrenwith hypoxic-ischemic injury and cerebral palsy, such as visuospatialdeficits, points to a distributed neural systems abnormality. This iswhy our treatment is based on networks. Others have used EEG-basedfunctional connectivity to guide their TMS treatment of ASD. Incomparison, results after administering a TMS treatment protocol usingmethods described herein indicate that patients under an anatomicallyprecise technique would experience longer-lasting clinical improvements.

The systems and methods herein may be used for patients suffering fromdementia. For example, FIGS. 72A-72D illustrate a baseline RSFC area mappre-TMS treatment according to embodiments described herein, with FIGS.73A-73D illustrates a baseline map after the fMRI guided treatmentaccording to embodiments described herein. FIGS. 74A-74D illustrate anexemplary control group having the same age and sex as the patient fromFIGS. 72A through 73D. The illustrations are averaged over 5 exemplarysubjects for the sake of illustration. The RSFC analysis according toexemplary methods described herein was run on five patients. Circuitwere identified for targeted TMS treatment according to embodimentsdescribed herein including the following regions within thecingulo-opercular, somatomotor, default, frontoparietal and dorsalattention networks for stimulation: Left Hemisphere: semantic retrieval:lexical retrieval: FOP5, FOP4, FOP3, FOP2; TE1a, TE1m, TE1p, PHT, TE2a.The treatment protocol were excitatory, at 130% amplitude of individualmotor threshold. The patients experienced symptoms includingimprovements in retrieval of lexical/semantic information starting frommoderate severe (score 5) improving to mild (score 3) in all subjects.Exemplary maps provided for different RSFC areas are provided forexample. See FIGS. 72A-98D.

Embodiments described herein are believed to be effective at halting,slowing, and/or reversing the effects of Alzheimer's Disease (AD). Todate, pharmacological treatments have not obtained favorable results andthat has created room for non-pharmacological interventions to be usedfor this disease. TMS can induce changes in brain activity and causelong-term modifications in impaired neural networks. Such capabilitiesof TMS hold many promises for clinical intervention. Standard TMS cancause changes in cortical excitability, increases brain plasticity, andfacilitates the recovery by the reorganization of impaired neuralnetworks responsible for cognitive impairment. However, standard TMSdoes not offer individualized targeting capabilities that can berectified by using advanced MRI techniques. The methods described hereinuse fMRI to map the entire brain networks and then run a specialanalysis for each patient to find the right target for TMS treatmentwith the largest outcome.

As fMRI according to embodiments described herein may be used as atechnique to produce biomarker-surrogate for neurodegenerative diseases.It may identify the presence of AD and allow for tracking progression,severity, guide TMS treatment, and offer an assessment of treatmenteffects. Growing evidence shows that interventions in neurodegenerativedisorders must be applied in early or even pre-symptomatic phases of AD.As such, the use of a sensitive technique like fMRI for monitoringdisease progression based on quantitative measures combined withclinical features offers a reliable and easy-to-track biomarker in thisfield.

Embodiments described herein may be used to restore a patient'sfoundation and reduce or recover from anxiety, such as GeneralizedAnxiety Disorder (GAD). The fMRI technology and methods described hereinmay bring clarity to a patient's treatment plan by allowing insightsinto how the individual patient's brain is working.

The unique combination of network-focused TMS and fMRI according toembodiments described herein may help patients find relief from anxiety,depression, and many other disorders of the brain. Simply put, the MMimages described herein and associated methods of analyzing the MMimages may help identify unique brain function to create a treatmentplan unique to the patient. As described herein, fMRI measures brainactivity by detecting changes associated with blood flow, and dMRI usescomputational tractography to produce three-dimensional trajectoriesthrough the white matter within the brain volume. When an area of thebrain is in use, blood flow to that region increases. Since severalparts of the brain are key factors in the production of fear andanxiety, the fMRI helps identify the areas affected in the patient'sbrain. Embodiments illustrated herein show anxiety circuits. Two typesof brain circuits show two different biomarkers for anxiety includingfrontal lobes or parieto-occipital sulcus. Some patients have bothfeatures and some patients are more inclined to frontal orparieto-occipital circuits.

For example, FIGS. 128A-128D illustrate a baseline RSFC area map pre-TMStreatment according to embodiments described herein, with FIGS.129A-129D illustrate an RSFC map after the fMRI guided treatmentaccording to embodiments described herein. The illustrations areaveraged over five subjects. The RSFC analysis according to methodsdescribed herein was run on five patients. The embodiments describedherein identified circuits for treatment with TMS including thefollowing regions within the frontoparietal, cingulo-opercular anddefault mode networks for stimulation: right hemisphere: dorsolateralprefrontal, area 9-46d, right. The treatment protocol were inhibitory,at 90-100% amplitude of individual motor threshold (adjusted bypenetration depth and type of coil used). The patients experiencedpsychological/physiological anxiety and panic attacks from a severescore of 6 to a minimal score of 2. Exemplary maps provided fordifferent RSFC areas are provided for example. See FIGS. 128A through137D

For example, FIGS. 138A-138D illustrate a baseline RSFC area map pre-TMStreatment according to embodiments described herein, with FIGS.139A-139D illustrating an SFC map after the fMRI guided treatment. Theillustrations are averaged over five subjects. The RSFC analysisaccording to methods described herein was run on five patients.Embodiments described herein identified circuits for treatment with TMSincluding the following regions within the frontoparietal, default modeand cingulo-opercular networks for stimulation: left hemisphere,parieto-occipital sulcus areas, area POS2, left. Embodiments of thetreatment protocol were inhibitory, at 90-100% amplitude of individualmotor threshold (adjusted by penetration depth and type of coil used).The patients experienced psychological/physiological anxiety and panicattacks from a severe score of 6 to a minimal score of 2. Exemplary mapsfor different RSFC areas are provided. See FIGS. 138A through 147D.

The systems and methods described herein may be used to treat bipolardisorder, also known as manic-depressive illness, which is a braindisorder that causes unusual shifts in mood, energy, activity levels,and the ability to carry out day-to-day tasks. Types of bipolar disorderinclude Bipolar I Disorder, Bipolar II Disorder, Cyclothymic Disorder,and Other Specified and Unspecified Bipolar and Related Disorders.

With fMRI imaging according to embodiments described herein, thenetwork(s) not functioning properly that causes manic episodes to occurcan be identified. With this technology, methods according toembodiments described herein may treat the specific areas precisely witha personalized treatment plan and precise neuronavigation.

Embodiments described herein may be used to treat ADHD.Attention-deficit hyperactivity disorder (ADHD) is a disordercharacterized by persistent and inappropriate levels of over-activity,impulsivity, and inattention.

Two partially segregated attention networks may be involved in ADHD: thedorsal attention network (DAN) and the ventral attention network (VAN).Both networks are deeply involved in the attentional regulatory systemsin the brain. The DAN is centered in the bilateral intraparietal sulcus(IPS) and the junction of the precentral and superior frontal sulcus(frontal eye fields, FEF), and enables the control of spatial attentionby selecting sensory stimuli based on internal goals or expectations,and linking these goals to the appropriate motor responses. One of themajor symptoms of ADHD, i.e., failure to ignore extraneous stimuli, isassociated with the loss of functional connection (FC) in the DAN. TheVAN is anchored in the right temporo-parietal junction (TPJ) and theventral frontal cortex (VFC), and reorients attention to salientbehaviorally relevant stimuli. In this regard, fMRI studies of ADHD haverevealed a significant hypo-activation in the VAN and DAN compared withthat in healthy controls, which is related to ADHD.

While task-based fMRI studies have made an important contribution to theunderstanding of brain function in ADHD, resting-state fMRI studies havealso revealed hypo-connectivity in the DAN and VAN in children andadults with ADHD. Therefore, the network cause of ADHD may be related tohypo-connectivity in the attention networks, including the DAN and VAN.

Treatments according to embodiments described herein may be guided byresting-state fMRI and not task-based fMRI because the way a networkresponds to external stimuli (task-based studies) depends on the brainnetwork's connectivity at rest (resting-state fMRI studies). As such,through computational mappings according to embodiments describedherein, the method can identify and modulate the way these networksrespond when engaged in attention demanding tasks. Moreover,resting-state fMRI may be easier to implement, allowing theidentification of functional brain networks with greater sensitivitythan task-based fMRI.

Systems and methods herein may be used to treat drug addiction. Imagingstudies have discovered specific networks that cause the three stages ofthe addiction cycle. The main components of the networks are centeredaround the ventral tegmental area and ventral striatum for the bingestage. The structure that plays a dominant role in the withdrawal phaseis the amygdala.

A number of networks are involved in the anticipation stage: Craving iscontrolled by the cingulate gyms, orbitofrontal cortex, dorsal striatum,prefrontal cortex, amygdala, and hippocampus; Loss of inhibitory controlis caused by malfunctioning of the insula, the dorsolateral prefrontal,and inferior frontal cortices.

A sequence of neuroplasticity events from the ventral to the striatumand orbitofrontal cortex cause dysregulation of the prefrontal cortex,cingulate gyms, and amygdala, governing the transition to addiction. Theidentification of the neural networks involved in the transition stagesof addiction have also offered insight into the vulnerability todeveloping addiction.

The underlying mechanism of TMS-induced effects that makes it aneffective treatment for SUD patients is that it modulates themaladaptive brain networks of addiction. Embodiments of the methodsdescribed herein may use this network-level action supported by manypreclinical and clinical findings to develop an even more effective TMStechnique, including using resting-state functional connectivity MRI orfMRI guided TMS.

The methods described herein include mapping the brain networks and thenusing network analysis anchored on the deep brain regions implicated inaddiction. This allows the methods described herein to determine acounterpart to the nodes on the malfunctioning network in the cortexwhich will become targets of neuronavigation-guided TMS treatment.

Embodiments described herein may also include fMRI to assess theseverity of the addiction, as well as to guide the TMS treatment. ThisfMRI-guided TMS is a state-of-the-art, highly complex technique thatuses infrared technology to precisely target the affected structures inthe brain. Through this technique, embodiments described herein mayoffer multiple targets to apply TMS, which is highly effective insmoking and substance use cessation.

Upon completion of the treatment, the method may also include the usefMRI for objectively assessing the treatment effects. fMRI may becomplemented with evidence-based self-help intervention,cognitive-behavioral interventions, and nicotine/substance replacementtherapy. The major advantage of fMRI is that it directly engages thedysregulation of motivational networks.

Since such dysregulation is caused by heightened incentive salience andhabit formation, reward deficits, and compromised executive function inthree stages, it engages the networks identifiable by fMRI andaccessible by TMS. It is established that rewarding effects of thesubstance—such as the development of incentive salience and drug-seekinghabits in the binge stage—involve changes in dopamine and opioidpeptides in the basal ganglia. Since direct stimulation of basal gangliais not possible by TMS due to the weak intensity of its electric fieldin the deep brain regions, targeting secondary regions of subcorticalareas anatomically connected to the DLPFC is only possible through theinformation that fMRI provides.

The analysis of fMRI according to embodiments described herein may findregions within the left DLPFC that can be used as target areas fortreating SUDs.

Embodiments may also be used in reducing drug consumption.

Embodiments described herein may apply the same strategy in treatingnegative emotional states. Negative emotional states in thewithdrawal-effect stage are caused by decreases in the function of thedopamine component of the reward system, and recruitment of brain stressneurotransmitters such as corticotropin-releasing factor and dynorphin.In the amygdala network, seed-based calculations may be used to findeffective cortical targets by fMRI to increase the efficacy of TMS intreating negative emotional states.

Similarly, the same approach may be used to suppress the craving anddeficits in executive function in the anticipation stage. Here, TMStargets are selected using fMRI knowing that this stage involves thedysregulation of afferent projections from the prefrontal cortex andinsula, including glutamate, to the basal ganglia and extended amygdala.As such, the system, methods, and algorithms have been very effective inthe treatment of addiction.

Embodiments described herein are believed to assist in eating disorders(EDs). fMRI TMS according to embodiments of the method described hereinmay be used to identify and correct the network(s) causing an eatingdisorder.

Eating palatable food increases activation in regions involved in rewardsuch as the ventral and dorsal striatum, midbrain, amygdala, andorbitofrontal cortex. Increased or decreased functional resting-stateconnectivity has also been observed in EDs compared to controls,implicating networks associated with executive function, rewardprocessing, and perception. Choosing low-fat vs. high-fat foodsincreases connectivity between dorsal caudate and DLPFC regions inpatients with AN, which is implicated in actual food intake eaten, thusmaking the DLPFC a region of considerable interest.

By identifying the underlying network associated with the disorderthrough fMRI imaging and the embodiments described herein, thebiological side of this disorder can be treated.

Embodiments described herein may be used to treat OCD.

Neuroimaging studies have provided strong evidence that OCD involvesneural circuits. The most viable candidate for the culprit network inthe pathophysiology of OCD is cortico-striato-thalamo-cortical (CSTC)circuits.

CSTC networks are believed to be involved in multiple cognitivefunctions such as inhibition of impulsive behavior, modulation of motoractivity, and assignment of attention. Imaging studies have also shownthat CSTC circuits have several interconnected circuits involvingfronto-cortical and subcortical brain areas. These pathways oppose eachother, giving the thalamus either: an inhibitory function causingreduction of movement through activation of the indirect pathway, or netexcitation causing an increase of movement by activating the directpathway.

Different CSTC networks are responsible for determining specific motorand cognitive functions. These selections are made by the specificfronto-cortical area included in the network.

The relationship between fronto-cortical areas and the basal ganglia maydetermine which actions are selected and which are suppressed.Stimulation or inhibition of appropriate behavior sequences depends onthe change in the balance of activity between direct and indirectpathways. Failure in eliminating dysfunctional behavior sequences causesOCD symptoms.

OCD patients may have dysfunction in the core neural processes performedby CSTC circuits, e.g., response inhibition and sensorimotor gating.This means that patients with OCD are biased to perform habits at theexpense of goal-directed actions. It is possible that differentpopulations of striatal neurons differentially regulate the direct andindirect basal ganglia pathways, causing stereotypic motor behaviors.

The direct pathways (i.e., striatum, substantia nigra, globus pallidusinterna) and indirect pathways (i.e., striatum, subthalamic nucleus,globus pallidus externa) may contribute to thalamic communication withthe cortex and in the generation of motor patterns. This understandingexplains why OCD symptoms may be caused by excess activity in directversus indirect OFC-subcortical networks. fMRI may be used to detect theactivity of the whole brain with special attention for the involvednetworks in OCD. The method may then be used to determine the targetsspecific to each individual patient based on the malfunctioning ofdifferent regions. These targets that vary amongst patients will then betreated by TMS for normalizing the functioning of the involved networks.

Embodiments described herein may be used to treat PTSD, which is anincapacitating condition with symptoms of nightmares or flashbacks,avoidance and hyperarousal following traumatic experiences.

The methods described herein including fMRI may be used to assess therole of three fundamental brain networks, namely the default modenetwork (DMN), central executive (CEN), and salience (SN) in theunderstanding of higher cognitive functioning. Hence, the technique thatcan address the issue of impairment of the functioning of these networkshas the opportunity to make a big difference in PTSD. In spite of thesuccess of standard TMS in treating PTSD, the fact that neural networksare not affected homogeneously calls for opting for a personalizedtreatment. Some embodiments of the disclosed method use resting-statefunctional connectivity MRI (rsFC MM) as the guide to personalize TMStherapy.

Embodiments may include fMRI-TMS at a wide range of frequencies 1-20 HzrTMS to theta burst (continuous/intermittent TBS) on specific targets inthe right and/or left DLPFC. iTBS is a high-frequency rTMS that deliversbrief trains of high-frequency pulses (50 Hz). The train is delivered ata 5 Hz frequency (every 200 msec), which is within the theta range ofEEG [4-7 Hz]. iTBS may be used for its many advantages such as itsability to deliver effective treatments in 3 min, compared with the37-min standard protocol for depression.

Embodiments described herein may be used to treat schizophrenia. TMS hasbeen used for treatment of schizophrenia. A version of TMS called deepTMS or dTMS has shown significant improvements in negative symptoms whenadministered to the DLPFC in schizophrenia. In this case, patients maybe treated using high-frequency (18 Hz) bilateral stimulation appliedover the DLPFC, bilaterally, with deep TMS coils. Objective measures ofimprovement such as the Scale for the Assessment of Negative Symptomsand the Positive and Negative Syndrome Scales may be used to ensure thereliability of results. Resting-state functional connectivity MRI orrsFC MRI is an advanced imaging technique that provides functionalconnectivity (FC) of the brain that may be used according to embodimentsdescribed herein to measure the abnormalities of brain networks inschizophrenia.

Comparing schizophrenia patients with healthy controls, individuals witha clinical high risk for psychosis (CHR) and schizophrenia patientsshowed hypo-connectivity between posterior insula (PI) and somatosensoryareas, and between dorsal anterior insula (dAI) and putamen.Furthermore, schizophrenia patients showed dAI and ventral anteriorinsula (vAI) hyper-connectivity with visual areas relative to controlsand CHR individuals. FC has offered evidence for the dysconnectivityhypothesis of schizophrenia.

As resting-state fMRI (rsfMRI) can map functional brain networks, suchas the default mode network (DMN), it makes the study of thesystems-level pathology of schizophrenia possible. The connectivity ofthe DMN may be altered in patients with schizophrenia. Specifically,features discovered by rsfMRI may include: hyper-connectivity of the DMNis the common consensus of rsFC MRI studies; alteredcortical-subcortical networks, including thalamocortical, frontolimbic,and cortico-cerebellar networks; reduced connectivity of the prefrontalcortex (PFC), particularly intra-PFC connectivity; patterns offunctional connectivity within auditory/language networks and the basalganglia correlate to specific clinical symptoms, includingauditory-verbal hallucinations and delusions.

Embodiments described herein may use rsFC MM and correlation analysis toidentify targets for TMS treatment of schizophrenia. dAI functionalconnectivity with superior temporal gyms may positively correlate withpositive symptoms of CHR. Furthermore, vAI connectivity with DLPFC maynegatively correlate with the severity of the symptoms of first-episodeschizophrenia. The methods described herein may give the capability ofusing rsFC MRI to map the whole-brain network topology and to use graphtheory. Functional brain networks in schizophrenia may be characterizedby reduced small-worldness, lower degree connectivity of brain hubs, anddecreased modularity.

The sensitivity of functional connectivity is sufficient to detectdifferences in unaffected relatives, suggesting that functionaldysconnectivity is an endophenotype related to genetic risk forschizophrenia. As we have broad support for dysconnectivity theories ofschizophrenia, this feature of rsFC MRI may be used to identify targetsfor TMS treatment.

The methods described herein may be used to treat stroke and other brainlesions. There may be beneficial effects of repetitive transcranialmagnetic stimulation (rTMS) on the left DLPFC for treating variousneuropsychiatric or neuropsychological disorders.

In some embodiments, the disclosed method may target specific brainregions. For example, the default mode network (DMN), cognitive controlnetwork (CCN) and affective network (AN) may include adepression-related increase in functional connectivity (FC) in the samedorsal region (i.e., dorsal medial prefrontal) called the dorsal nexus.This result suggests that depressive symptoms are not associated with aspecific network but rather the dysfunction of several brain networks.Further, PSD has been shown to cause changes in FC in DMN and AN. Thesenetworks may be involved in the pathogenesis of PSD.

Patients with first acute ischemic stroke onset were analyzed for theperformance of a fMRI and found that the functional connectivity (FC) ofthe motor network in acute ischemic stroke is independently associatedwith functional outcomes. Specifically, the FC between ipsilesionalprimary motor cortex (MI) and contralesional dorsal premotor area (PMd),were independently associated with unfavorable outcomes, whereas the FCof the default mode network was not different between groups. Theseresults showed that interhemispheric FC of the motor network is anindependent predictor of functional outcomes in patients with acuteischemic stroke. High-frequency rTMS on the left DLPFC may enhancelow-frequency resting-state brain activity in the target site and remotesites, as reflected by fALFF and FC. This way TMS may be used to reachremote sites affected by stroke.

Inversely, fMRI can be used to find the exact location of the corticalcounterpart of the networks in which an inaccessible brain region hasbeen affected by stroke. Embodiments described herein may comprise acomplete brain assessment for determining target locations for TMStreatment. Embodiments described herein include the comparison ofcovariation of fMRI data between regions and networks of a patient'sbrain. Embodiments may include a complete assessment of these regionsnetworks according to embodiments described herein. Embodiments may alsoprovide focus to specific regions and networks based on the knownassociated functional connections in relation to a given condition,symptom, or illness. The focus may be to reduce the computationsrequired in assessing the functional connectivity of the brain, and/orin providing particular attention, such as segmentation into smallerregions for more specific targeting of areas that are already believedto be beneficial for TMS treatment in relationship to a given symptom ordisease. Such pre-knowledge of functional relationships as describedherein may be used to determine the regions and/or region sizes of brainregions as described herein in making comparisons and assessments. Suchpre-knowledge of functional relationships as described herein may beused to eliminate some comparisons and/or enlarge regions forcomparisons for areas that are not of interest or not believed to befunctionally related or relevant to the given symptom or disease.

The conventional TMS approach that is FDA approved uses the 5-cm rulefor TMS targeting. This approach does not include any guidance throughimaging, and is not personalized to account for variations of brainregions between individuals. Some attempts to use fMRI to guide TMSdetermine the most anticorrelated region of the DLPFC with the subgenualcingulate to detect a target for TMS stimulation for Depression. Thisapproach focuses on only two specific regions of the brain with only asingle relationship between these regions. Current embodiments describedherein may include a more complete brain assessment to detect braincircuits including multiple targets for TMS and LIFUS. Embodiments mayinclude an assessment of the entire brain as opposed to a specificallyfocused approach on the DLPFC. Cortical brain circuits may be selectedfor target stimulation with TMS. Subcortical brain circuits may beselected for targeted stimulation with LIFUS. Embodiments describedherein may include a circuit-based approach that assesses multiple andinterconnected brain regions for stimulation as opposed to the singleregion application on the DLPFC. Embodiments described herein may beused in depression, but also other brain injuries, such as TBIs or otherbrain lesions. Embodiments described herein identify and extractamplitude and frequency of brain activity within/between brain networksin order to analyze connectivity between circuits.

Specific brain conditions are shown and described herein with respect tofunctional connection analysis and the resulting likely regions ofinterest for target treatments for stimulation. Specific examples areprovided herein, which are not intended to be limiting. Structuralanalysis may be added or used as described herein for any braincondition to find abnormalities and/or determine target locations forstimulation.

Total Cortical Gray Matter Volume and White Surface Total Area, in aprospective case series in neurodevelopmental disorders (such as AutismSpectrum Disorders) and neurodegenerative diseases (such as Dementias,Alzheimer's disease, etc.).

Tables 1-2 illustrate improvements of structural connectivity areprovided here as examples of the success of the use of structuralconnectivity in analyzing neurodevelopmental conditions.

TABLE 2 White White Surface Matter Total Area Volume % mm{circumflexover ( )}2 (before) (after) change P1 93033.3 95126.1 2.2 P2 94151.896268.3 2.199 P3 92178.6 94221.5 2.168 P4 93173.8 95578.7 2.516 P592227.1 95232.1 3.155 Mean 92932.8 95325.2 2.51 Sd 809.16 743.68 0.42

TABLE 1 Cortical Cortical Grey Grey Total Matter Matter % mm{circumflexover ( )}3 (before) (after) change P1 559513 563756 0.753 P2 540735549931 1.672 P3 561866 569011 1.256 P4 523163 527801 0.879 P5 543242552101 1.605 mean 542252 549711 1.35 Sd 15738.1 15938.3 0.41

Table 1 provides a sample of five (5) patients with Autism SpectrumDisorder between the ages of 18 and 22 years old. All patients showed anincrease in total cortical grey matter volume after completion of twomonths of fMRI-guided TMS treatment (5 times per week) when the methodused structural connectivity. Individual and averaged measurements aredisplayed at baseline and after treatments. In healthy controls, thetotal cortical grey matter volume loss is about one (1) percent peryear.

Table 2 provides a sample of five (5) patients with Autism spectrumDisorder between the ages of 18 and 22 years old. All patients showed anincrease in white surface total area after completion of two month fMRIguided TMS treatments (5 treatments per week) using structuralconnectivity analysis according to embodiments described herein.Individual and averaged measurements are displayed at baseline and aftertreatments. In healthy children, the white surface total area generallyincreases over a three year interval (from nine years old to 12 yearsold) by 1.7%.

Tables 3-4 illustrate improvements of structural connectivity areprovided here as examples of the success of the use of structuralconnectivity in analyzing neurodegenerative disorders.

TABLE 3 Cortical Cortical Grey Grey Total Matter Matter % mm{circumflexover ( )}3 (before) (after) change P1 559513 563756 0.753 P2 540735549931 1.672 P3 561866 569011 1.256 P4 523163 527801 0.879 P5 543242552101 1.605 mean 542252 549711 1.35 Sd 15738.1 15938.3 0.41

TABLE 4 White White Surface Matter Total Area Volume % mm{circumflexover ( )}2 (before) (after) change P1 93033.3 95126.1 2.2 P2 94151.896268.3 2.199 P3 92178.6 94221.5 2.168 P4 93173.8 95578.7 2.516 P592227.1 95232.1 3.155 Mean 92932.8 95325.2 2.51 Sd 809.16 743.68 0.42

Table 3 provides a sample of five patients with Dementias (Alzheimer'sdisease) of patients in the age range of 69 to 72 years old. Allpatients showed an increase in total cortical grey matter volume aftercompletion of six month of fMRI guided TMS treatments of 5 weeks perweek. Individual and averaged measurements are displayed at baseline andafter treatments. Local gray matter loss rates (5.3 plus or minus 2.3%per year in AD verse 0.9 plus or minus 0.9 percent per year in controls.

Table 4 provides a sample of five (5) patients with Dementias(Alzheimer's disease) of a patient age range of 68 to 72 years old. Allpatients showed an increase in white surface total area after completionof six months of fMRI guided TMS treatments (at 5 times per week).Individual and averaged measurements are displayed at baseline and aftertreatment. In healthy individuals ranging from 30 to 90 years of age, a26% reduction in white matter tissue volume has been observed, relativeto a 14% reduction in gray matter tissue volume.

The computing devices described herein are non-conventional systems atleast because of the use of non-conventional component parts and/or theuse of non-conventional algorithms, processes, and methods embodied, atleast partially, in the programming instructions stored and/or executedby the computing devices. For example, embodiments may useconfigurations of and processes involving a unique magnetic devices foradministering magnetic fields to a patient according to methods andalgorithms for specific treatment targeting as described herein,configurations of and processes involving a unique transcranial magneticstimulation device or low frequency intensity focused ultrasound devicewith and without high precision navigation, unique processes andalgorithms for determining specific brain circuits of a patient's brainthat is personalized per patient, unique configurations and processesfor targeting and placing the TMS for application to the specific braincircuits personalized per patient, or combinations thereof. The systemsand methods described herein also include algorithms for identifying andextracting amplitude and frequency of brain activity within and betweenbrain networks for precise and personalized TMS treatment. Embodimentsmay be used to identify target locations for TMS treatment ofneurological and psychiatric conditions such as, without limitation,traumatic brain injury, brain lesions, psychoses, depression,maintenance. Autism, dementia, among others. Exemplar embodimentsdescribed herein may also or alternatively be used in prevention and notsimply pathology, such as prevention of cognitive decline due to age.Embodiments may be used to improve the length of a treatment effect sothat the beneficial therapeutic treatment effects may last longer.Embodiments may include combinations of functional connection and/orstructural connection to determine target treatment areas that arepersonalized to the individual. The use of a combination of functionaland/or structural connections may provide a more complete analysis of anindividual's brain function based on the individual and/or the braincondition to find target locations for stimulation that are most likelyto make improvements to a patient's condition.

Embodiments comprise a non-transitory computer-accessible medium havingstored thereon computer-executable instructions for determining one ormore target regions for transcranial magnetic stimulation (TMS)treatment of a patient, wherein the computer-executable instructions areconfigured when executed by one or more processors to perform a method.A method for determining one or more target regions for transcranialmagnetic stimulation (TMS treatment of a patient may include steps for:receiving functional magnetic resonance imaging (fMRI) data of a head ofthe patient; analyzing functional connections of the patient's brainthrough analysis of the fMRI data by determining changes in anycombination of a first fluctuation in amplitude of the fMRI imagingdata, a second fluctuation in frequency of the fMRI data, or a thirdfluctuation in frequency relative to amplitude of the fMRI data; anddetermining one or more target regions for TMS treatment of the patientbased on the determination of any combination of the first fluctuation,the second fluctuation, or the third fluctuation.

Embodiments of the non-transitory computer-accessible medium,instructions, or methods may include any combination of: comparing thedetermination of any combination of the first fluctuation, the secondfluctuation, or the third fluctuation with measurements of a healthcontrol group matching an age range and gender of the patient.

Embodiments of the non-transitory computer-accessible medium,instructions, or methods may include the analyzing the functionalconnections of the patient's brain including the first fluctuation,second fluctuation, or third fluctuation, includes any combination of:determining activation, correlation, covariation, or a combinationthereof matrices between different brain networks of the patient'sbrain; matrices between regions within a network of the patient's brain;matrices between regions within a network and all other regions of thenetwork combined; matrices between different brain regions within anetwork and all other regions within the same network; and/or matricesbetween different brain regions of different networks.

The non-transitory computer-accessible medium, instructions, or methodsmay include any combination of: selecting a first plurality of brainregions having a low change in activation, correlation, covariation, ora combination thereof with a larger number of brain networks or regions;selecting a second plurality of brain regions having a high change inactivation, correlation, covariation, or a combination thereof with alarge number of brain networks or regions; comparing the first pluralityof brain regions and the second plurality of brain regions with brainregions from a health control group matching an age range and gender ofthe patient, and/or selecting the brain regions above a first thresholdvalue and below a second threshold value to create a potential targetgroup of brain regions for TMS treatment.

The non-transitory computer-accessible medium, instructions, or methodsmay include the first threshold value within two standard deviationsabove a mean of the healthy control group, and the second thresholdvalue is within two standard deviations below the mean of the healthycontrol group.

The non-transitory computer-accessible medium, instructions, or methodsmay include any combination of the potential target group of brainregions for TMS treatment is compared with a second control groupmatching the patient's symptoms, diagnosis, or a combination thereof,and determining a subset from the potential target group of brainregions by determining which of the regions has the greatest change infMRI data from pre-TMS treatment to post-TMS treatment from the secondcontrol group; and/or administering TMS treatment to the patient basedon at least one parameter from a treatment plan of a member from thesecond control group having a brain region of the greatest change infMRI data from pre-TMS treatment to post-TMS treatment.

The non-transitory computer-accessible medium, instructions, or methodsmay include receiving diffusion-weighted magnetic resonance imaging(dMRI) data of a head of the patient; analyzing structural connectionsof the patient's brain through analysis of the dMitI data by comparingstrength of a white matter connection between combinations of parcelswithin the patient's brain; wherein determining one or more targetregions for TMS treatment of the patient is based on the analysis ofstructural connections. The non-transitory computer-accessible mediumand/or methods may include determining one or more target regionscomprises selecting target locations for stimulation with a highernumber of white matter connections as compared to other potentialtargets.

The non-transitory computer-accessible medium, instructions, or methodsmay include the analysis of structural connections of the patient'sbrain is conducted after the analysis of functional connections and thedetermination of the one or more target regions are first determinedbased on the functional connections and then a final subset are selectedusing the analysis of structural connections.

The non-transitory computer-accessible medium, instructions, or methodsmay include performing functional connectivity analysis by: defining aplurality of brain networks of the patient and a plurality of brainregions of the patient; comprising comparing covariation between a brainnetwork and each of another brain network of the plurality of brainnetworks for each of the plurality of brain networks of the patient,comparing covariation between a first brain region to each of anotherbrain region within a same brain network as the first brain region foreach of the plurality of brain regions, comparing covariation between asecond brain region to each of another brain region within a differentbrain network as the second brain region for each of the plurality ofbrain regions, comparing covariation between a third brain region toeach of another brain network different from the brain network in whichthe third brain region is contained for each of the plurality of brainregions; selecting a first set of regions in which a lowest change incovariation comparison occurs with a largest number of brain regions ofthe plurality of regions; selecting a second set of regions in whichlowest change in covariation comparison occurs with a largest number ofbrain regions of the plurality of regions; comparing the first set ofregions and the second set of regions with a healthy control group anddetermining a first set of potential targets from the first set ofregions and the second set of regions that are outside of a determinednormal range as compared to the healthy control group; and determine atarget set of regions from the comparison of the first set of regionsand the second set of regions with the healthy control groups.

The non-transitory computer-accessible medium, instructions, or methodsmay include determining one or more target regions for transcranialmagnetic stimulation (TMS) treatment of a patient for treating a mentaldisorder, the method comprising: receiving functional magnetic resonanceimaging (fMRI) data of a head of the patient; analyzing functionalconnections of the patient's brain through analysis of the fMRI data bydetermining changes in any combination of a first fluctuation inamplitude of the fMRI imaging data, a second fluctuation in frequency ofthe fMRI data, or a third fluctuation in frequency relative to amplitudeof the fMRI data; determining one or more target regions for TMStreatment of the patient based on the determination of any combinationof the first fluctuation, the second fluctuation, or the thirdfluctuation; applying TMS treatment at the one or more target regions;and improving a mental disorder of the patient.

The non-transitory computer-accessible medium, instructions, or methodsmay include the mental disorder being selected from Alzheimer's disease,anxiety, obsessive compulsive disorder, post-traumatic stress disorder,schizophrenia, insomnia, eating disorders, cognitive impairment, drugaddiction, depression, attention deficit hyperactivity disorder,attention deficit disorder, bipolar disorder, autism spectrum disorder,neurodevelopmental disorders, and psychoses.

The non-transitory computer-accessible medium, instructions, or methodsmay include receiving diffusion-weighted magnetic resonance imaging(dMRT) data of a head of the patient; analyzing structural connectionsof the patient's brain through analysis of the dMRT data by comparingstrength of a white matter connection between combinations of parcelswithin the patient's brain; determining one or more target regions forTMS treatment of the patient based on the analysis of structuralconnections.

The non-transitory computer-accessible medium, instructions, or methodsmay include receiving diffusion-weighted magnetic resonance imaging(dMRT) data of a head of the patient; analyzing the structuralconnection of the patient's brain through analysis of the dMRT data bygenerating a brain structural connectivity matrix constructed based onwhite matter tractography from the whole brain; and determining one ormore target regions for TMS treatment of the patient based on theanalysis of structural connections.

The non-transitory computer-accessible medium, instructions, or methodsmay include receiving fMRI data; construct covariation matrixes usingthe fMRI data; selecting a first set of potential targets with strongercovariation values with a first large group of brain regions, selectinga second set of potential targets with weaker covariation with a secondlarge group of brain regions, or selecting a combination of the firstset of potential targets and the second set of potential targets;receive dMRI data; use dMRI data to construct a structural connectivitymatrix; select target regions from the first set of potential targets,the second set of potential targets, or the first and second set ofpotential targets with a higher number of white matter connections.

The non-transitory computer-accessible medium, instructions, or methodsmay include receiving fMRI data; construct covariation matrixes usingthe fMRI data; selecting a first set of potential targets with strongercovariation values with a first large group of brain regions, selectinga second set of potential targets with weaker covariation with a secondlarge group of brain regions, or selecting a combination of the firstset of potential targets and the second set of potential targets;receive dMRI data; use dMRI data to construct a structural connectivitymatrix; select target regions from the first set of potential targets,the second set of potential targets, or the first and second set ofpotential targets with a higher number of white matter connections.

The non-transitory computer-accessible medium, instructions, or methodsmay include receive dMRT data; use dMRT data to construct a structuralconnectivity matrix; select a set of potential targets with a lowernumber of white matter connections; receiving fMRI data; constructcovariation matrixes using the fMRI data; selecting a first set oftargets from the set of potential targets with stronger covariationvalues with a first large group of brain regions, selecting a second setof targets from the set of potential targets with weaker covariationwith a second large group of brain regions, or selecting a combinationof the first set of targets and the second set of targets.

The non-transitory computer-accessible medium, instructions, or methodsmay include the structural data comprising T1w/T2w is used to constructthe structural connectivity matrix.

The system described herein can be based in software and/or hardware.While some specific embodiments of the invention have been shown theinvention is not to be limited to these embodiments. For example, mostfunctions performed by electronic hardware components may be duplicatedby software emulation. Thus, a software program written to accomplishthose same functions may emulate the functionality of the hardwarecomponents in input-output circuitry. The invention is to be understoodas not limited by the specific embodiments described herein, but only byscope of the appended claims.

Although embodiments of this invention have been fully described withreference to the accompanying drawings, various changes andmodifications will be apparent to those skilled in the art. Such changesand modifications are to be understood as being included within thescope of embodiments of this disclosure as defined by the appendedclaims. Any of the disclosed components may be used in any combination.For example, any component, feature, step or part may be integrated,separated, sub-divided, removed, duplicated, added, or used in anycombination and remain within the scope of the present disclosure.Embodiments are exemplary only, and provide an illustrative combinationof features, but are not limited thereto.

The foregoing merely illustrates the principles of the disclosure. Anyexamples set forth in this specification are not intended to be limitingand merely set forth some of the many possible embodiments for theappended claims. Those skilled in the art will readily recognize variousmodifications and changes that may be made without following the exampleembodiments and applications illustrated and described herein, andwithout departing from the true spirit and scope of the followingclaims.

All references cited and/or discussed in this specification areincorporated herein by reference in their entireties and to the sameextent as if each reference was individually incorporated by reference.

1-25. (canceled)
 26. A non-transitory computer-accessible medium havingstored thereon computer-executable instructions for determining one ormore target regions for transcranial magnetic stimulation (TMS)treatment of a patient, wherein the computer-executable instructions areconfigured when executed by one or more processors to perform a method,comprising: receiving functional magnetic resonance imaging (fMRI) dataof a head of the patient; analyzing functional connections of thepatient's brain through analysis of the fMRI data by determining changesin any combination of a first fluctuation in amplitude of the fMRIimaging data, a second fluctuation in frequency of the fMRI data, or athird fluctuation in frequency relative to amplitude of the fMRI data;and determining one or more target regions for TMS treatment of thepatient based on the determination of any combination of the firstfluctuation, the second fluctuation, or the third fluctuation.
 27. Thenon-transitory computer-accessible medium of claim 26, wherein thecomputer-executable instructions are further configured to perform:comparing the determination of any combination of the first fluctuation,the second fluctuation, or the third fluctuation with measurements of ahealth control group matching an age range and gender of the patient.28. The non-transitory computer-accessible medium of claim 26, whereinthe analyzing the functional connections of the patient's brainincluding the first fluctuation, second fluctuation, or thirdfluctuation, includes determining matrices of activation, correlation,covariation, or a combination thereof between different brain networksof the patient's brain.
 29. The non-transitory computer-accessiblemedium of claim 26, wherein the analyzing the functional connections ofthe patient's brain including the first fluctuation, second fluctuation,or third fluctuation, includes determining matrices of activation,correlation, covariation, or a combination thereof between regionswithin a network of the patient's brain.
 30. The non-transitorycomputer-accessible medium of claim 26, wherein the analyzing thefunctional connections of the patient's brain including the firstfluctuation, second fluctuation, or third fluctuation, includesdetermining matrices of activation, correlation, covariation, or acombination thereof between regions within a network and all otherregions of the network combined.
 31. The non-transitorycomputer-accessible medium of claim 26, wherein the analyzing thefunctional connections of the patient's brain including the firstfluctuation, second fluctuation, or third fluctuation, includesdetermining matrices of activation, correlation, covariation, or acombination thereof between different brain regions within a network andall other regions within the same network.
 32. The non-transitorycomputer-accessible medium of claim 26, wherein the analyzing thefunctional connections of the patient's brain including the firstfluctuation, second fluctuation, or third fluctuation, includesdetermining the matrices of activation, correlation, covariation, or acombination thereof between different brain regions of differentnetworks.
 33. The non-transitory computer-accessible medium of claim 26,wherein the computer-executable instructions are further configured toperform: selecting a first plurality of brain regions having a lowchange in activation, correlation, covariation, or a combination thereofwith a larger number of brain networks or regions.
 34. Thenon-transitory computer-accessible medium of claim 33, wherein thecomputer-executable instructions are further configured to perform:selecting a second plurality of brain regions having a high change inactivation, correlation, covariation, or a combination thereof with alarge number of brain networks or regions.
 35. The non-transitorycomputer-accessible medium of claim 34, wherein the computer-executableinstructions are further configured to perform: comparing the firstplurality of brain regions and the second plurality of brain regionswith brain regions from a health control group matching an age range andgender of the patient, and selecting the brain regions above a firstthreshold value and below a second threshold value to create a potentialtarget group of brain regions for TMS treatment.
 36. The non-transitorycomputer-accessible medium of claim 35, wherein the first thresholdvalue is within two standard deviations above a mean of the healthycontrol group, and the second threshold value is within two standarddeviations below the mean of the healthy control group.
 37. Thenon-transitory computer-accessible medium of claim 36, wherein thepotential target group of brain regions for TMS treatment is comparedwith a second control group matching the patient's symptoms, diagnosis,or a combination thereof, and determining a subset from the potentialtarget group of brain regions by determining which of the regions hasthe greatest change in fMRI data from pre-TMS treatment to post-TMStreatment from the second control group.
 38. The non-transitorycomputer-accessible medium of claim 37, wherein the computer-executableinstructions are further configured to perform: administering TMStreatment to the patient based on at least one parameter from atreatment plan of a member from the second control group having a brainregion of the greatest change in fMRI data from pre-TMS treatment topost-TMS treatment.
 39. The non-transitory computer-accessible medium ofclaim 26, wherein the computer-executable instructions are furtherconfigured to perform: receiving diffusion-weighted magnetic resonanceimaging (dMRI) data of a head of the patient; analyzing structuralconnections of the patient's brain through analysis of the dMitI data bycomparing strength of a white matter connection between combinations ofparcels within the patient's brain; wherein determining one or moretarget regions for TMS treatment of the patient is based on the analysisof structural connections.
 40. The non-transitory computer-accessiblemedium of claim 39, wherein the determining one or more target regionscomprises selecting target locations for stimulation with a highernumber of white matter connections as compared to other potentialtargets.
 41. The non-transitory computer-accessible medium of claim 40,wherein the analysis of structural connections of the patient's brain isconducted after the analysis of functional connections and thedetermination of the one or more target regions are first determinedbased on the functional connections and then a final subset are selectedusing the analysis of structural connections.
 42. A method of treating apatient using transcranial magnetic stimulation (TMS) treatment,comprising: determining one or more target regions for stimulation usingthe non-transitory computer-accessible medium of claim 26; andstimulating the patient using TMS at the one or more target regions. 43.A non-transitory computer-accessible medium having stored thereoncomputer-executable instructions for determining one or more targetregions for transcranial magnetic stimulation (TMS) treatment of apatient, wherein the computer-executable instructions are configuredwhen executed by one or more processors to perform a method, comprising:receiving fMRI data; construct covariation matrixes using the fMRI data;selecting a first set of potential targets with stronger covariationvalues with a first large group of brain regions, selecting a second setof potential targets with weaker covariation with a second large groupof brain regions, or selecting a combination of the first set ofpotential targets and the second set of potential targets; receive dMRIdata; use dMRI data to construct a structural connectivity matrix;select target regions from the first set of potential targets, thesecond set of potential targets, or the first and second set ofpotential targets with a higher number of white matter connections. 44.A non-transitory computer-accessible medium having stored thereoncomputer-executable instructions for determining one or more targetregions for transcranial magnetic stimulation (TMS) treatment of apatient, wherein the computer-executable instructions are configuredwhen executed by one or more processors to perform a method, comprising:receive dMRI data; use dMRI data to construct a structural connectivitymatrix; select a set of potential targets with a lower number of whitematter connections; receiving fMRI data; construct covariation matrixesusing the fMRI data; selecting a first set of targets from the set ofpotential targets with stronger covariation values with a first largegroup of brain regions, selecting a second set of targets from the setof potential targets with weaker covariation with a second large groupof brain regions, or selecting a combination of the first set of targetsand the second set of targets.
 45. The method of claim 44, whereinstructural data comprising T1w/T2w is used to construct the structuralconnectivity matrix.